[UdemyCourseDownloader] Machine Learning A-Z™ Hands-On Python & R In Data Science

磁力链接/BT种子名称

[UdemyCourseDownloader] Machine Learning A-Z™ Hands-On Python & R In Data Science

磁力链接/BT种子简介

种子哈希：2bc1b318098cf07a4735c1267e9a6daab90860fe
文件大小： 6.84G
已经下载：610次
下载速度：极快
收录时间：2022-01-09
最近下载：2025-09-01

移花宫入口

移花宫.com邀月.com怜星.com花无缺.comyhgbt.icuyhgbt.top

磁力链接下载

magnet:?xt=urn:btih:2BC1B318098CF07A4735C1267E9A6DAAB90860FE
推荐使用PIKPAK网盘下载资源，10TB超大空间，不限制资源，无限次数离线下载，视频在线观看

下载BT种子文件

磁力链接 迅雷下载 PIKPAK在线播放 世界之窗 91视频 含羞草 欲漫涩 逼哩逼哩 成人快手 51品茶 抖阴破解版 极乐禁地 91短视频 暗网Xvideo TikTok成人版 PornHub 听泉鉴鲍 少女日记 草榴社区 哆哔涩漫 呦乐园 萝莉岛 悠悠禁区 拔萝卜 疯马秀

最近搜索

文件列表

  • 12 Logistic Regression/096 Logistic Regression in R - Step 5.mp4 98.3 MB
  • 31 Artificial Neural Networks/225 ANN in Python - Step 2.mp4 89.0 MB
  • 17 Decision Tree Classification/123 Decision Tree Classification in R.mp4 71.5 MB
  • 14 Support Vector Machine (SVM)/105 SVM in R.mp4 68.5 MB
  • 18 Random Forest Classification/127 Random Forest Classification in R.mp4 67.2 MB
  • 32 Convolutional Neural Networks/256 CNN in Python - Step 9.mp4 65.4 MB
  • 18 Random Forest Classification/126 Random Forest Classification in Python.mp4 65.1 MB
  • 07 Support Vector Regression (SVR)/068 SVR in Python.mp4 63.1 MB
  • 05 Multiple Linear Regression/045 Multiple Linear Regression in Python - Backward Elimination - HOMEWORK.mp4 62.0 MB
  • 27 Upper Confidence Bound (UCB)/178 Upper Confidence Bound in R - Step 3.mp4 60.6 MB
  • 36 Kernel PCA/274 Kernel PCA in R.mp4 59.3 MB
  • 24 Apriori/161 Apriori in R - Step 3.mp4 59.3 MB
  • 08 Decision Tree Regression/073 Decision Tree Regression in R.mp4 59.0 MB
  • 13 K-Nearest Neighbors (K-NN)/101 K-NN in R.mp4 58.5 MB
  • 28 Thompson Sampling/183 Thompson Sampling in Python - Step 1.mp4 58.2 MB
  • 15 Kernel SVM/111 Kernel SVM in Python.mp4 57.5 MB
  • 06 Polynomial Regression/063 Polynomial Regression in R - Step 3.mp4 57.5 MB
  • 06 Polynomial Regression/058 Polynomial Regression in Python - Step 3.mp4 57.1 MB
  • 05 Multiple Linear Regression/046 Multiple Linear Regression in Python - Backward Elimination - Homework Solution.mp4 56.9 MB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/210 Natural Language Processing in R - Step 10.mp4 56.8 MB
  • 27 Upper Confidence Bound (UCB)/174 Upper Confidence Bound in Python - Step 3.mp4 56.3 MB
  • 12 Logistic Regression/090 Logistic Regression in Python - Step 5.mp4 55.7 MB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/015 Categorical Data.mp4 55.4 MB
  • 24 Apriori/159 Apriori in R - Step 1.mp4 55.4 MB
  • 15 Kernel SVM/112 Kernel SVM in R.mp4 55.4 MB
  • 09 Random Forest Regression/076 Random Forest Regression in Python.mp4 55.3 MB
  • 05 Multiple Linear Regression/041 Multiple Linear Regression in Python - Step 1.mp4 54.7 MB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/197 Natural Language Processing in Python - Step 8.mp4 54.5 MB
  • 09 Random Forest Regression/077 Random Forest Regression in R.mp4 54.4 MB
  • 35 Linear Discriminant Analysis (LDA)/271 LDA in R.mp4 53.8 MB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/201 Natural Language Processing in R - Step 1.mp4 53.7 MB
  • 28 Thompson Sampling/185 Thompson Sampling in R - Step 1.mp4 53.5 MB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/017 Splitting the Dataset into the Training set and Test set.mp4 53.4 MB
  • 05 Multiple Linear Regression/051 Multiple Linear Regression in R - Backward Elimination - HOMEWORK.mp4 53.3 MB
  • 16 Naive Bayes/113 Bayes Theorem.mp4 52.9 MB
  • 31 Artificial Neural Networks/234 ANN in R - Step 1.mp4 52.3 MB
  • 21 K-Means Clustering/139 K-Means Clustering in Python.mp4 52.2 MB
  • 16 Naive Bayes/119 Naive Bayes in R.mp4 52.2 MB
  • 04 Simple Linear Regression/032 Simple Linear Regression in R - Step 4.mp4 51.5 MB
  • 24 Apriori/162 Apriori in Python - Step 1.mp4 49.7 MB
  • 39 XGBoost/285 XGBoost in R.mp4 49.6 MB
  • 13 K-Nearest Neighbors (K-NN)/100 K-NN in Python.mp4 49.3 MB
  • 07 Support Vector Regression (SVR)/067 SVR Intuition.mp4 48.9 MB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/190 Natural Language Processing in Python - Step 1.mp4 48.3 MB
  • 35 Linear Discriminant Analysis (LDA)/270 LDA in Python.mp4 47.6 MB
  • 05 Multiple Linear Regression/049 Multiple Linear Regression in R - Step 2.mp4 47.4 MB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/018 Feature Scaling.mp4 46.8 MB
  • 27 Upper Confidence Bound (UCB)/173 Upper Confidence Bound in Python - Step 2.mp4 46.7 MB
  • 31 Artificial Neural Networks/237 ANN in R - Step 4 (Last step).mp4 45.9 MB
  • 38 Model Selection/278 k-Fold Cross Validation in R.mp4 45.8 MB
  • 08 Decision Tree Regression/072 Decision Tree Regression in Python.mp4 45.5 MB
  • 32 Convolutional Neural Networks/244 Step 4 - Full Connection.mp4 44.8 MB
  • 14 Support Vector Machine (SVM)/104 SVM in Python.mp4 43.7 MB
  • 32 Convolutional Neural Networks/242 Step 2 - Pooling.mp4 42.2 MB
  • 04 Simple Linear Regression/028 Simple Linear Regression in Python - Step 4.mp4 41.3 MB
  • 31 Artificial Neural Networks/228 ANN in Python - Step 5.mp4 41.3 MB
  • 27 Upper Confidence Bound (UCB)/172 Upper Confidence Bound in Python - Step 1.mp4 40.9 MB
  • 17 Decision Tree Classification/122 Decision Tree Classification in Python.mp4 40.7 MB
  • 24 Apriori/160 Apriori in R - Step 2.mp4 40.7 MB
  • 38 Model Selection/279 Grid Search in Python - Step 1.mp4 40.1 MB
  • 31 Artificial Neural Networks/236 ANN in R - Step 3.mp4 39.7 MB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/209 Natural Language Processing in R - Step 9.mp4 39.5 MB
  • 31 Artificial Neural Networks/224 ANN in Python - Step 1 - Installing Theano Tensorflow and Keras.mp4 39.3 MB
  • 24 Apriori/163 Apriori in Python - Step 2.mp4 39.1 MB
  • 28 Thompson Sampling/180 Thompson Sampling Intuition.mp4 39.1 MB
  • 21 K-Means Clustering/140 K-Means Clustering in R.mp4 38.7 MB
  • 06 Polynomial Regression/060 Python Regression Template.mp4 38.6 MB
  • 34 Principal Component Analysis (PCA)/267 PCA in R - Step 3.mp4 38.5 MB
  • 38 Model Selection/281 Grid Search in R.mp4 37.3 MB
  • 24 Apriori/164 Apriori in Python - Step 3.mp4 37.0 MB
  • 06 Polynomial Regression/057 Polynomial Regression in Python - Step 2.mp4 36.8 MB
  • 24 Apriori/157 Apriori Intuition.mp4 36.7 MB
  • 15 Kernel SVM/108 The Kernel Trick.mp4 36.4 MB
  • 32 Convolutional Neural Networks/251 CNN in Python - Step 4.mp4 36.3 MB
  • 27 Upper Confidence Bound (UCB)/177 Upper Confidence Bound in R - Step 2.mp4 35.8 MB
  • 31 Artificial Neural Networks/231 ANN in Python - Step 8.mp4 35.7 MB
  • 27 Upper Confidence Bound (UCB)/176 Upper Confidence Bound in R - Step 1.mp4 35.7 MB
  • 07 Support Vector Regression (SVR)/069 SVR in R.mp4 35.4 MB
  • 36 Kernel PCA/273 Kernel PCA in Python.mp4 35.0 MB
  • 32 Convolutional Neural Networks/246 Softmax Cross-Entropy.mp4 34.8 MB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/199 Natural Language Processing in Python - Step 10.mp4 34.5 MB
  • 38 Model Selection/277 k-Fold Cross Validation in Python.mp4 34.4 MB
  • 05 Multiple Linear Regression/040 Multiple Linear Regression Intuition - Step 5.mp4 34.4 MB
  • 06 Polynomial Regression/062 Polynomial Regression in R - Step 2.mp4 33.9 MB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/014 Missing Data.mp4 33.7 MB
  • 34 Principal Component Analysis (PCA)/260 Principal Component Analysis (PCA) Intuition.mp4 33.7 MB
  • 39 XGBoost/284 XGBoost in Python - Step 2.mp4 33.5 MB
  • 34 Principal Component Analysis (PCA)/262 PCA in Python - Step 1.mp4 33.5 MB
  • 06 Polynomial Regression/056 Polynomial Regression in Python - Step 1.mp4 33.2 MB
  • 06 Polynomial Regression/065 R Regression Template.mp4 32.9 MB
  • 30 -------------------- Part 8 Deep Learning --------------------/213 What is Deep Learning.mp4 32.8 MB
  • 16 Naive Bayes/118 Naive Bayes in Python.mp4 32.7 MB
  • 16 Naive Bayes/114 Naive Bayes Intuition.mp4 32.6 MB
  • 32 Convolutional Neural Networks/240 Step 1 - Convolution Operation.mp4 32.5 MB
  • 34 Principal Component Analysis (PCA)/265 PCA in R - Step 1.mp4 32.1 MB
  • 32 Convolutional Neural Networks/248 CNN in Python - Step 1.mp4 32.1 MB
  • 27 Upper Confidence Bound (UCB)/169 The Multi-Armed Bandit Problem.mp4 31.7 MB
  • 21 K-Means Clustering/135 K-Means Clustering Intuition.mp4 31.4 MB
  • 31 Artificial Neural Networks/215 The Neuron.mp4 31.3 MB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/193 Natural Language Processing in Python - Step 4.mp4 31.2 MB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/188 Natural Language Processing Intuition.mp4 31.1 MB
  • 38 Model Selection/280 Grid Search in Python - Step 2.mp4 30.9 MB
  • 32 Convolutional Neural Networks/239 What are convolutional neural networks.mp4 30.9 MB
  • 27 Upper Confidence Bound (UCB)/170 Upper Confidence Bound (UCB) Intuition.mp4 30.7 MB
  • 31 Artificial Neural Networks/223 Business Problem Description.mp4 30.7 MB
  • 12 Logistic Regression/084 Logistic Regression Intuition.mp4 30.6 MB
  • 34 Principal Component Analysis (PCA)/266 PCA in R - Step 2.mp4 30.4 MB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/012 Importing the Dataset.mp4 30.0 MB
  • 06 Polynomial Regression/064 Polynomial Regression in R - Step 4.mp4 29.9 MB
  • 31 Artificial Neural Networks/232 ANN in Python - Step 9.mp4 29.9 MB
  • 31 Artificial Neural Networks/233 ANN in Python - Step 10.mp4 29.8 MB
  • 10 Evaluating Regression Models Performance/080 Evaluating Regression Models Performance - Homeworks Final Part.mp4 29.7 MB
  • 04 Simple Linear Regression/025 Simple Linear Regression in Python - Step 1.mp4 29.3 MB
  • 32 Convolutional Neural Networks/257 CNN in Python - Step 10.mp4 29.1 MB
  • 12 Logistic Regression/094 Logistic Regression in R - Step 3.mp4 28.8 MB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/191 Natural Language Processing in Python - Step 2.mp4 28.8 MB
  • 10 Evaluating Regression Models Performance/081 Interpreting Linear Regression Coefficients.mp4 28.7 MB
  • 35 Linear Discriminant Analysis (LDA)/268 Linear Discriminant Analysis (LDA) Intuition.mp4 28.3 MB
  • 31 Artificial Neural Networks/218 How do Neural Networks learn.mp4 27.8 MB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/019 And here is our Data Preprocessing Template.mp4 27.1 MB
  • 21 K-Means Clustering/137 K-Means Selecting The Number Of Clusters.mp4 26.9 MB
  • 18 Random Forest Classification/124 Random Forest Classification Intuition.mp4 26.9 MB
  • 34 Principal Component Analysis (PCA)/264 PCA in Python - Step 3.mp4 26.7 MB
  • 05 Multiple Linear Regression/043 Multiple Linear Regression in Python - Step 3.mp4 26.7 MB
  • 08 Decision Tree Regression/070 Decision Tree Regression Intuition.mp4 26.6 MB
  • 25 Eclat/167 Eclat in R.mp4 26.5 MB
  • 04 Simple Linear Regression/030 Simple Linear Regression in R - Step 2.mp4 26.1 MB
  • 04 Simple Linear Regression/026 Simple Linear Regression in Python - Step 2.mp4 25.8 MB
  • 01 Welcome to the course/005 Installing Python and Anaconda (Mac Linux Windows).mp4 25.1 MB
  • 05 Multiple Linear Regression/044 Multiple Linear Regression in Python - Backward Elimination - Preparation.mp4 25.0 MB
  • 31 Artificial Neural Networks/217 How do Neural Networks work.mp4 24.7 MB
  • 05 Multiple Linear Regression/048 Multiple Linear Regression in R - Step 1.mp4 24.6 MB
  • 01 Welcome to the course/007 Installing R and R Studio (Mac Linux Windows).mp4 24.3 MB
  • 22 Hierarchical Clustering/143 Hierarchical Clustering Using Dendrograms.mp4 23.9 MB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/196 Natural Language Processing in Python - Step 7.mp4 23.2 MB
  • 34 Principal Component Analysis (PCA)/263 PCA in Python - Step 2.mp4 23.1 MB
  • 05 Multiple Linear Regression/052 Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4 23.0 MB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/202 Natural Language Processing in R - Step 2.mp4 22.7 MB
  • 17 Decision Tree Classification/120 Decision Tree Classification Intuition.mp4 22.7 MB
  • 10 Evaluating Regression Models Performance/079 Adjusted R-Squared Intuition.mp4 22.5 MB
  • 39 XGBoost/283 XGBoost in Python - Step 1.mp4 22.4 MB
  • 22 Hierarchical Clustering/148 HC in Python - Step 4.mp4 22.4 MB
  • 06 Polynomial Regression/061 Polynomial Regression in R - Step 1.mp4 22.2 MB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/010 Get the dataset.mp4 22.2 MB
  • 04 Simple Linear Regression/027 Simple Linear Regression in Python - Step 3.mp4 21.5 MB
  • 19 Evaluating Classification Models Performance/131 CAP Curve.mp4 21.3 MB
  • 14 Support Vector Machine (SVM)/102 SVM Intuition.mp4 20.9 MB
  • 16 Naive Bayes/116 Naive Bayes Intuition (Extras).mp4 19.9 MB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/198 Natural Language Processing in Python - Step 9.mp4 19.8 MB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/194 Natural Language Processing in Python - Step 5.mp4 19.7 MB
  • 31 Artificial Neural Networks/219 Gradient Descent.mp4 19.4 MB
  • 31 Artificial Neural Networks/235 ANN in R - Step 2.mp4 19.1 MB
  • 06 Polynomial Regression/059 Polynomial Regression in Python - Step 4.mp4 18.5 MB
  • 12 Logistic Regression/091 Python Classification Template.mp4 18.4 MB
  • 12 Logistic Regression/097 R Classification Template.mp4 18.4 MB
  • 22 Hierarchical Clustering/142 Hierarchical Clustering How Dendrograms Work.mp4 18.3 MB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/208 Natural Language Processing in R - Step 8.mp4 18.1 MB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/203 Natural Language Processing in R - Step 3.mp4 17.7 MB
  • 12 Logistic Regression/086 Logistic Regression in Python - Step 1.mp4 17.7 MB
  • 31 Artificial Neural Networks/220 Stochastic Gradient Descent.mp4 17.6 MB
  • 32 Convolutional Neural Networks/254 CNN in Python - Step 7.mp4 17.5 MB
  • 05 Multiple Linear Regression/037 Multiple Linear Regression Intuition - Step 3.mp4 17.4 MB
  • 22 Hierarchical Clustering/141 Hierarchical Clustering Intuition.mp4 17.3 MB
  • 22 Hierarchical Clustering/147 HC in Python - Step 3.mp4 17.0 MB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/206 Natural Language Processing in R - Step 6.mp4 16.9 MB
  • 12 Logistic Regression/092 Logistic Regression in R - Step 1.mp4 16.5 MB
  • 15 Kernel SVM/109 Types of Kernel Functions.mp4 16.5 MB
  • 09 Random Forest Regression/074 Random Forest Regression Intuition.mp4 16.4 MB
  • 22 Hierarchical Clustering/146 HC in Python - Step 2.mp4 16.3 MB
  • 15 Kernel SVM/107 Mapping to a higher dimension.mp4 16.1 MB
  • 21 K-Means Clustering/136 K-Means Random Initialization Trap.mp4 16.1 MB
  • 19 Evaluating Classification Models Performance/128 False Positives False Negatives.mp4 15.9 MB
  • 31 Artificial Neural Networks/230 ANN in Python - Step 7.mp4 15.6 MB
  • 12 Logistic Regression/093 Logistic Regression in R - Step 2.mp4 15.6 MB
  • 31 Artificial Neural Networks/216 The Activation Function.mp4 15.5 MB
  • 31 Artificial Neural Networks/226 ANN in Python - Step 3.mp4 15.3 MB
  • 01 Welcome to the course/002 Why Machine Learning is the Future.mp4 15.2 MB
  • 32 Convolutional Neural Networks/241 Step 1(b) - ReLU Layer.mp4 14.8 MB
  • 28 Thompson Sampling/181 Algorithm Comparison UCB vs Thompson Sampling.mp4 14.8 MB
  • 12 Logistic Regression/089 Logistic Regression in Python - Step 4.mp4 14.5 MB
  • 22 Hierarchical Clustering/151 HC in R - Step 2.mp4 14.5 MB
  • 05 Multiple Linear Regression/050 Multiple Linear Regression in R - Step 3.mp4 14.5 MB
  • 22 Hierarchical Clustering/145 HC in Python - Step 1.mp4 14.4 MB
  • 22 Hierarchical Clustering/154 HC in R - Step 5.mp4 14.3 MB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/011 Importing the Libraries.mp4 14.2 MB
  • 16 Naive Bayes/115 Naive Bayes Intuition (Challenge Reveal).mp4 13.9 MB
  • 19 Evaluating Classification Models Performance/132 CAP Curve Analysis.mp4 13.6 MB
  • 05 Multiple Linear Regression/034 Dataset Business Problem Description.mp4 13.2 MB
  • 27 Upper Confidence Bound (UCB)/175 Upper Confidence Bound in Python - Step 4.mp4 13.0 MB
  • 32 Convolutional Neural Networks/252 CNN in Python - Step 5.mp4 13.0 MB
  • 32 Convolutional Neural Networks/253 CNN in Python - Step 6.mp4 12.5 MB
  • 31 Artificial Neural Networks/229 ANN in Python - Step 6.mp4 12.5 MB
  • 12 Logistic Regression/095 Logistic Regression in R - Step 4.mp4 12.3 MB
  • 04 Simple Linear Regression/021 How to get the dataset.mp4 12.3 MB
  • 05 Multiple Linear Regression/033 How to get the dataset.mp4 12.3 MB
  • 06 Polynomial Regression/055 How to get the dataset.mp4 12.3 MB
  • 07 Support Vector Regression (SVR)/066 How to get the dataset.mp4 12.3 MB
  • 08 Decision Tree Regression/071 How to get the dataset.mp4 12.3 MB
  • 09 Random Forest Regression/075 How to get the dataset.mp4 12.3 MB
  • 12 Logistic Regression/085 How to get the dataset.mp4 12.3 MB
  • 13 K-Nearest Neighbors (K-NN)/099 How to get the dataset.mp4 12.3 MB
  • 14 Support Vector Machine (SVM)/103 How to get the dataset.mp4 12.3 MB
  • 15 Kernel SVM/110 How to get the dataset.mp4 12.3 MB
  • 16 Naive Bayes/117 How to get the dataset.mp4 12.3 MB
  • 17 Decision Tree Classification/121 How to get the dataset.mp4 12.3 MB
  • 18 Random Forest Classification/125 How to get the dataset.mp4 12.3 MB
  • 21 K-Means Clustering/138 How to get the dataset.mp4 12.3 MB
  • 22 Hierarchical Clustering/144 How to get the dataset.mp4 12.3 MB
  • 24 Apriori/158 How to get the dataset.mp4 12.3 MB
  • 25 Eclat/166 How to get the dataset.mp4 12.3 MB
  • 27 Upper Confidence Bound (UCB)/171 How to get the dataset.mp4 12.3 MB
  • 28 Thompson Sampling/182 How to get the dataset.mp4 12.3 MB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/189 How to get the dataset.mp4 12.3 MB
  • 31 Artificial Neural Networks/222 How to get the dataset.mp4 12.3 MB
  • 32 Convolutional Neural Networks/247 How to get the dataset.mp4 12.3 MB
  • 34 Principal Component Analysis (PCA)/261 How to get the dataset.mp4 12.3 MB
  • 35 Linear Discriminant Analysis (LDA)/269 How to get the dataset.mp4 12.3 MB
  • 36 Kernel PCA/272 How to get the dataset.mp4 12.3 MB
  • 38 Model Selection/276 How to get the dataset.mp4 12.3 MB
  • 39 XGBoost/282 How to get the dataset.mp4 12.3 MB
  • 04 Simple Linear Regression/029 Simple Linear Regression in R - Step 1.mp4 12.1 MB
  • 04 Simple Linear Regression/031 Simple Linear Regression in R - Step 3.mp4 12.0 MB
  • 28 Thompson Sampling/184 Thompson Sampling in Python - Step 2.mp4 11.8 MB
  • 12 Logistic Regression/087 Logistic Regression in Python - Step 2.mp4 11.6 MB
  • 31 Artificial Neural Networks/221 Backpropagation.mp4 11.5 MB
  • 25 Eclat/165 Eclat Intuition.mp4 11.2 MB
  • 04 Simple Linear Regression/023 Simple Linear Regression Intuition - Step 1.mp4 11.0 MB
  • 13 K-Nearest Neighbors (K-NN)/098 K-Nearest Neighbor Intuition.mp4 11.0 MB
  • 22 Hierarchical Clustering/153 HC in R - Step 4.mp4 10.7 MB
  • 22 Hierarchical Clustering/152 HC in R - Step 3.mp4 10.4 MB
  • 22 Hierarchical Clustering/149 HC in Python - Step 5.mp4 10.4 MB
  • 05 Multiple Linear Regression/042 Multiple Linear Regression in Python - Step 2.mp4 10.3 MB
  • 01 Welcome to the course/001 Applications of Machine Learning.mp4 10.3 MB
  • 10 Evaluating Regression Models Performance/078 R-Squared Intuition.mp4 10.3 MB
  • 31 Artificial Neural Networks/227 ANN in Python - Step 4.mp4 10.2 MB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/207 Natural Language Processing in R - Step 7.mp4 10.1 MB
  • 28 Thompson Sampling/186 Thompson Sampling in R - Step 2.mp4 10.0 MB
  • 27 Upper Confidence Bound (UCB)/179 Upper Confidence Bound in R - Step 4.mp4 10.0 MB
  • 06 Polynomial Regression/054 Polynomial Regression Intuition.mp4 9.9 MB
  • 32 Convolutional Neural Networks/255 CNN in Python - Step 8.mp4 9.4 MB
  • 19 Evaluating Classification Models Performance/129 Confusion Matrix.mp4 9.3 MB
  • 22 Hierarchical Clustering/150 HC in R - Step 1.mp4 9.0 MB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/195 Natural Language Processing in Python - Step 6.mp4 8.7 MB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/204 Natural Language Processing in R - Step 4.mp4 8.6 MB
  • 12 Logistic Regression/088 Logistic Regression in Python - Step 3.mp4 8.4 MB
  • 32 Convolutional Neural Networks/245 Summary.mp4 8.3 MB
  • 04 Simple Linear Regression/022 Dataset Business Problem Description.mp4 8.1 MB
  • 32 Convolutional Neural Networks/249 CNN in Python - Step 2.mp4 7.6 MB
  • 15 Kernel SVM/106 Kernel SVM Intuition.mp4 6.7 MB
  • 04 Simple Linear Regression/024 Simple Linear Regression Intuition - Step 2.mp4 6.3 MB
  • 32 Convolutional Neural Networks/238 Plan of attack.mp4 6.2 MB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/205 Natural Language Processing in R - Step 5.mp4 6.1 MB
  • 05 Multiple Linear Regression/038 Multiple Linear Regression Intuition - Step 4.mp4 5.6 MB
  • 31 Artificial Neural Networks/214 Plan of attack.mp4 5.0 MB
  • 19 Evaluating Classification Models Performance/130 Accuracy Paradox.mp4 4.4 MB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/192 Natural Language Processing in Python - Step 3.mp4 4.4 MB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/009 Welcome to Part 1 - Data Preprocessing.mp4 3.7 MB
  • 32 Convolutional Neural Networks/243 Step 3 - Flattening.mp4 3.4 MB
  • 32 Convolutional Neural Networks/250 CNN in Python - Step 3.mp4 2.9 MB
  • 01 Welcome to the course/004 Machine-Learning-A-Z-Q-A.pdf 2.4 MB
  • 05 Multiple Linear Regression/036 Multiple Linear Regression Intuition - Step 2.mp4 2.1 MB
  • 05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 1.mp4 2.1 MB
  • 25 Eclat/167 Eclat.zip 49.7 kB
  • 16 Naive Bayes/113 Bayes Theorem-ja.srt 38.2 kB
  • 18 Random Forest Classification/127 Random Forest Classification in R-ja.srt 38.2 kB
  • 36 Kernel PCA/274 Kernel PCA in R-ja.srt 37.7 kB
  • 08 Decision Tree Regression/073 Decision Tree Regression in R-ja.srt 37.5 kB
  • 32 Convolutional Neural Networks/256 CNN in Python - Step 9-ja.srt 36.5 kB
  • 24 Apriori/161 Apriori in R - Step 3-ja.srt 36.5 kB
  • 35 Linear Discriminant Analysis (LDA)/271 LDA in R-ja.srt 36.4 kB
  • 18 Random Forest Classification/126 Random Forest Classification in Python-ja.srt 36.3 kB
  • 31 Artificial Neural Networks/225 ANN in Python - Step 2-ja.srt 36.3 kB
  • 07 Support Vector Regression (SVR)/068 SVR in Python-ja.srt 36.3 kB
  • 24 Apriori/159 Apriori in R - Step 1-ja.srt 36.0 kB
  • 16 Naive Bayes/113 Bayes Theorem-es.srt 35.5 kB
  • 06 Polynomial Regression/058 Polynomial Regression in Python - Step 3-ja.srt 35.2 kB
  • 16 Naive Bayes/113 Bayes Theorem-pt.srt 34.8 kB
  • 16 Naive Bayes/113 Bayes Theorem-it.srt 34.8 kB
  • 28 Thompson Sampling/183 Thompson Sampling in Python - Step 1-ja.srt 34.8 kB
  • 06 Polynomial Regression/063 Polynomial Regression in R - Step 3-ja.srt 34.5 kB
  • 32 Convolutional Neural Networks/244 Step 4 - Full Connection-ja.srt 34.4 kB
  • 17 Decision Tree Classification/123 Decision Tree Classification in R-ja.srt 34.3 kB
  • 18 Random Forest Classification/127 Random Forest Classification in R-es.srt 34.2 kB
  • 12 Logistic Regression/090 Logistic Regression in Python - Step 5-ja.srt 34.1 kB
  • 36 Kernel PCA/274 Kernel PCA in R-es.srt 34.1 kB
  • 18 Random Forest Classification/127 Random Forest Classification in R-pt.srt 34.0 kB
  • 16 Naive Bayes/113 Bayes Theorem-en.srt 33.9 kB
  • 28 Thompson Sampling/185 Thompson Sampling in R - Step 1-ja.srt 33.8 kB
  • 38 Model Selection/278 k-Fold Cross Validation in R-ja.srt 33.8 kB
  • 15 Kernel SVM/111 Kernel SVM in Python-ja.srt 33.8 kB
  • 18 Random Forest Classification/127 Random Forest Classification in R-it.srt 33.7 kB
  • 36 Kernel PCA/274 Kernel PCA in R-pt.srt 33.7 kB
  • 28 Thompson Sampling/180 Thompson Sampling Intuition-ja.srt 33.7 kB
  • 08 Decision Tree Regression/073 Decision Tree Regression in R-pt.srt 33.6 kB
  • 08 Decision Tree Regression/073 Decision Tree Regression in R-es.srt 33.6 kB
  • 36 Kernel PCA/274 Kernel PCA in R-it.srt 33.5 kB
  • 12 Logistic Regression/096 Logistic Regression in R - Step 5-ja.srt 33.4 kB
  • 21 K-Means Clustering/139 K-Means Clustering in Python-ja.srt 33.3 kB
  • 08 Decision Tree Regression/073 Decision Tree Regression in R-it.srt 33.3 kB
  • 16 Naive Bayes/113 Bayes Theorem-tr.srt 33.1 kB
  • 31 Artificial Neural Networks/234 ANN in R - Step 1-ja.srt 33.0 kB
  • 18 Random Forest Classification/127 Random Forest Classification in R-tr.srt 33.0 kB
  • 27 Upper Confidence Bound (UCB)/174 Upper Confidence Bound in Python - Step 3-ja.srt 33.0 kB
  • 32 Convolutional Neural Networks/256 CNN in Python - Step 9-es.srt 32.8 kB
  • 35 Linear Discriminant Analysis (LDA)/271 LDA in R-es.srt 32.8 kB
  • 06 Polynomial Regression/058 Polynomial Regression in Python - Step 3-es.srt 32.7 kB
  • 36 Kernel PCA/274 Kernel PCA in R-tr.srt 32.7 kB
  • 24 Apriori/162 Apriori in Python - Step 1-ja.srt 32.7 kB
  • 18 Random Forest Classification/126 Random Forest Classification in Python-es.srt 32.7 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/017 Splitting the Dataset into the Training set and Test set-ja.srt 32.5 kB
  • 24 Apriori/161 Apriori in R - Step 3-es.srt 32.5 kB
  • 39 XGBoost/285 XGBoost in R-ja.srt 32.4 kB
  • 09 Random Forest Regression/077 Random Forest Regression in R-ja.srt 32.4 kB
  • 09 Random Forest Regression/076 Random Forest Regression in Python-ja.srt 32.4 kB
  • 18 Random Forest Classification/126 Random Forest Classification in Python-pt.srt 32.4 kB
  • 24 Apriori/159 Apriori in R - Step 1-es.srt 32.3 kB
  • 35 Linear Discriminant Analysis (LDA)/271 LDA in R-pt.srt 32.3 kB
  • 07 Support Vector Regression (SVR)/068 SVR in Python-es.srt 32.3 kB
  • 06 Polynomial Regression/058 Polynomial Regression in Python - Step 3-it.srt 32.2 kB
  • 06 Polynomial Regression/058 Polynomial Regression in Python - Step 3-pt.srt 32.2 kB
  • 32 Convolutional Neural Networks/256 CNN in Python - Step 9-pt.srt 32.2 kB
  • 35 Linear Discriminant Analysis (LDA)/271 LDA in R-it.srt 32.1 kB
  • 32 Convolutional Neural Networks/256 CNN in Python - Step 9-it.srt 32.1 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/210 Natural Language Processing in R - Step 10-ja.srt 32.0 kB
  • 08 Decision Tree Regression/073 Decision Tree Regression in R-tr.srt 32.0 kB
  • 18 Random Forest Classification/126 Random Forest Classification in Python-it.srt 32.0 kB
  • 24 Apriori/161 Apriori in R - Step 3-it.srt 32.0 kB
  • 24 Apriori/161 Apriori in R - Step 3-pt.srt 31.9 kB
  • 18 Random Forest Classification/127 Random Forest Classification in R-en.srt 31.9 kB
  • 24 Apriori/159 Apriori in R - Step 1-pt.srt 31.8 kB
  • 06 Polynomial Regression/063 Polynomial Regression in R - Step 3-es.srt 31.8 kB
  • 31 Artificial Neural Networks/225 ANN in Python - Step 2-es.srt 31.8 kB
  • 07 Support Vector Regression (SVR)/068 SVR in Python-it.srt 31.8 kB
  • 07 Support Vector Regression (SVR)/068 SVR in Python-pt.srt 31.8 kB
  • 18 Random Forest Classification/126 Random Forest Classification in Python-tr.srt 31.7 kB
  • 28 Thompson Sampling/183 Thompson Sampling in Python - Step 1-es.srt 31.7 kB
  • 08 Decision Tree Regression/073 Decision Tree Regression in R-en.srt 31.6 kB
  • 24 Apriori/159 Apriori in R - Step 1-it.srt 31.6 kB
  • 36 Kernel PCA/274 Kernel PCA in R-en.srt 31.5 kB
  • 06 Polynomial Regression/063 Polynomial Regression in R - Step 3-pt.srt 31.5 kB
  • 32 Convolutional Neural Networks/246 Softmax Cross-Entropy-ja.srt 31.5 kB
  • 31 Artificial Neural Networks/225 ANN in Python - Step 2-pt.srt 31.4 kB
  • 28 Thompson Sampling/183 Thompson Sampling in Python - Step 1-it.srt 31.4 kB
  • 28 Thompson Sampling/183 Thompson Sampling in Python - Step 1-pt.srt 31.4 kB
  • 35 Linear Discriminant Analysis (LDA)/271 LDA in R-tr.srt 31.4 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/015 Categorical Data-ja.srt 31.4 kB
  • 06 Polynomial Regression/063 Polynomial Regression in R - Step 3-it.srt 31.3 kB
  • 06 Polynomial Regression/058 Polynomial Regression in Python - Step 3-tr.srt 31.3 kB
  • 31 Artificial Neural Networks/225 ANN in Python - Step 2-it.srt 31.2 kB
  • 35 Linear Discriminant Analysis (LDA)/270 LDA in Python-ja.srt 31.1 kB
  • 32 Convolutional Neural Networks/256 CNN in Python - Step 9-tr.srt 31.1 kB
  • 27 Upper Confidence Bound (UCB)/178 Upper Confidence Bound in R - Step 3-ja.srt 30.9 kB
  • 06 Polynomial Regression/058 Polynomial Regression in Python - Step 3-en.srt 30.9 kB
  • 12 Logistic Regression/090 Logistic Regression in Python - Step 5-es.srt 30.9 kB
  • 07 Support Vector Regression (SVR)/068 SVR in Python-tr.srt 30.8 kB
  • 38 Model Selection/278 k-Fold Cross Validation in R-es.srt 30.8 kB
  • 24 Apriori/161 Apriori in R - Step 3-tr.srt 30.8 kB
  • 12 Logistic Regression/090 Logistic Regression in Python - Step 5-pt.srt 30.7 kB
  • 24 Apriori/161 Apriori in R - Step 3-en.srt 30.7 kB
  • 17 Decision Tree Classification/123 Decision Tree Classification in R-es.srt 30.7 kB
  • 31 Artificial Neural Networks/225 ANN in Python - Step 2-tr.srt 30.6 kB
  • 24 Apriori/159 Apriori in R - Step 1-en.srt 30.6 kB
  • 24 Apriori/157 Apriori Intuition-ja.srt 30.5 kB
  • 12 Logistic Regression/090 Logistic Regression in Python - Step 5-it.srt 30.5 kB
  • 17 Decision Tree Classification/123 Decision Tree Classification in R-pt.srt 30.5 kB
  • 28 Thompson Sampling/185 Thompson Sampling in R - Step 1-es.srt 30.4 kB
  • 07 Support Vector Regression (SVR)/068 SVR in Python-en.srt 30.4 kB
  • 32 Convolutional Neural Networks/244 Step 4 - Full Connection-pt.srt 30.4 kB
  • 06 Polynomial Regression/063 Polynomial Regression in R - Step 3-en.srt 30.4 kB
  • 35 Linear Discriminant Analysis (LDA)/271 LDA in R-en.srt 30.4 kB
  • 24 Apriori/159 Apriori in R - Step 1-tr.srt 30.3 kB
  • 32 Convolutional Neural Networks/244 Step 4 - Full Connection-it.srt 30.3 kB
  • 18 Random Forest Classification/126 Random Forest Classification in Python-en.srt 30.3 kB
  • 38 Model Selection/278 k-Fold Cross Validation in R-it.srt 30.3 kB
  • 28 Thompson Sampling/185 Thompson Sampling in R - Step 1-pt.srt 30.3 kB
  • 12 Logistic Regression/096 Logistic Regression in R - Step 5-es.srt 30.3 kB
  • 12 Logistic Regression/090 Logistic Regression in Python - Step 5-tr.srt 30.3 kB
  • 32 Convolutional Neural Networks/244 Step 4 - Full Connection-es.srt 30.2 kB
  • 15 Kernel SVM/112 Kernel SVM in R-ja.srt 30.2 kB
  • 28 Thompson Sampling/185 Thompson Sampling in R - Step 1-it.srt 30.2 kB
  • 31 Artificial Neural Networks/215 The Neuron-ja.srt 30.2 kB
  • 38 Model Selection/278 k-Fold Cross Validation in R-pt.srt 30.2 kB
  • 17 Decision Tree Classification/123 Decision Tree Classification in R-it.srt 30.2 kB
  • 05 Multiple Linear Regression/051 Multiple Linear Regression in R - Backward Elimination - HOMEWORK-ja.srt 30.1 kB
  • 06 Polynomial Regression/063 Polynomial Regression in R - Step 3-tr.srt 30.1 kB
  • 32 Convolutional Neural Networks/256 CNN in Python - Step 9-en.srt 30.1 kB
  • 27 Upper Confidence Bound (UCB)/173 Upper Confidence Bound in Python - Step 2-ja.srt 30.0 kB
  • 12 Logistic Regression/096 Logistic Regression in R - Step 5-pt.srt 30.0 kB
  • 28 Thompson Sampling/183 Thompson Sampling in Python - Step 1-tr.srt 29.9 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/201 Natural Language Processing in R - Step 1-ja.srt 29.9 kB
  • 21 K-Means Clustering/139 K-Means Clustering in Python-es.srt 29.8 kB
  • 12 Logistic Regression/096 Logistic Regression in R - Step 5-it.srt 29.8 kB
  • 27 Upper Confidence Bound (UCB)/174 Upper Confidence Bound in Python - Step 3-es.srt 29.8 kB
  • 17 Decision Tree Classification/123 Decision Tree Classification in R-tr.srt 29.7 kB
  • 31 Artificial Neural Networks/225 ANN in Python - Step 2-en.srt 29.6 kB
  • 31 Artificial Neural Networks/234 ANN in R - Step 1-es.srt 29.6 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/210 Natural Language Processing in R - Step 10-es.srt 29.6 kB
  • 28 Thompson Sampling/183 Thompson Sampling in Python - Step 1-en.srt 29.6 kB
  • 28 Thompson Sampling/180 Thompson Sampling Intuition-es.srt 29.5 kB
  • 27 Upper Confidence Bound (UCB)/174 Upper Confidence Bound in Python - Step 3-pt.srt 29.4 kB
  • 21 K-Means Clustering/139 K-Means Clustering in Python-pt.srt 29.4 kB
  • 09 Random Forest Regression/077 Random Forest Regression in R-es.srt 29.4 kB
  • 27 Upper Confidence Bound (UCB)/174 Upper Confidence Bound in Python - Step 3-it.srt 29.3 kB
  • 15 Kernel SVM/111 Kernel SVM in Python-es.srt 29.3 kB
  • 35 Linear Discriminant Analysis (LDA)/270 LDA in Python-es.srt 29.3 kB
  • 12 Logistic Regression/090 Logistic Regression in Python - Step 5-en.srt 29.3 kB
  • 32 Convolutional Neural Networks/244 Step 4 - Full Connection-en.srt 29.3 kB
  • 38 Model Selection/278 k-Fold Cross Validation in R-tr.srt 29.2 kB
  • 12 Logistic Regression/096 Logistic Regression in R - Step 5-tr.srt 29.2 kB
  • 21 K-Means Clustering/139 K-Means Clustering in Python-it.srt 29.2 kB
  • 32 Convolutional Neural Networks/244 Step 4 - Full Connection-tr.srt 29.2 kB
  • 28 Thompson Sampling/180 Thompson Sampling Intuition-pt.srt 29.2 kB
  • 15 Kernel SVM/111 Kernel SVM in Python-pt.srt 29.1 kB
  • 31 Artificial Neural Networks/234 ANN in R - Step 1-pt.srt 29.1 kB
  • 09 Random Forest Regression/077 Random Forest Regression in R-pt.srt 29.1 kB
  • 24 Apriori/162 Apriori in Python - Step 1-es.srt 29.1 kB
  • 28 Thompson Sampling/185 Thompson Sampling in R - Step 1-tr.srt 29.1 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/197 Natural Language Processing in Python - Step 8-ja.srt 29.1 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/210 Natural Language Processing in R - Step 10-pt.srt 29.0 kB
  • 28 Thompson Sampling/180 Thompson Sampling Intuition-it.srt 29.0 kB
  • 09 Random Forest Regression/077 Random Forest Regression in R-it.srt 29.0 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/017 Splitting the Dataset into the Training set and Test set-es.srt 29.0 kB
  • 31 Artificial Neural Networks/234 ANN in R - Step 1-it.srt 28.9 kB
  • 39 XGBoost/285 XGBoost in R-es.srt 28.9 kB
  • 09 Random Forest Regression/076 Random Forest Regression in Python-es.srt 28.8 kB
  • 35 Linear Discriminant Analysis (LDA)/270 LDA in Python-pt.srt 28.8 kB
  • 15 Kernel SVM/111 Kernel SVM in Python-tr.srt 28.8 kB
  • 15 Kernel SVM/111 Kernel SVM in Python-it.srt 28.8 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/017 Splitting the Dataset into the Training set and Test set-pt.srt 28.7 kB
  • 12 Logistic Regression/096 Logistic Regression in R - Step 5-en.srt 28.7 kB
  • 17 Decision Tree Classification/123 Decision Tree Classification in R-en.srt 28.7 kB
  • 35 Linear Discriminant Analysis (LDA)/270 LDA in Python-it.srt 28.6 kB
  • 09 Random Forest Regression/076 Random Forest Regression in Python-pt.srt 28.6 kB
  • 05 Multiple Linear Regression/041 Multiple Linear Regression in Python - Step 1-ja.srt 28.6 kB
  • 38 Model Selection/278 k-Fold Cross Validation in R-en.srt 28.6 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/210 Natural Language Processing in R - Step 10-it.srt 28.6 kB
  • 28 Thompson Sampling/185 Thompson Sampling in R - Step 1-en.srt 28.5 kB
  • 39 XGBoost/285 XGBoost in R-it.srt 28.5 kB
  • 24 Apriori/162 Apriori in Python - Step 1-it.srt 28.5 kB
  • 24 Apriori/162 Apriori in Python - Step 1-pt.srt 28.5 kB
  • 05 Multiple Linear Regression/051 Multiple Linear Regression in R - Backward Elimination - HOMEWORK-pt.srt 28.5 kB
  • 05 Multiple Linear Regression/051 Multiple Linear Regression in R - Backward Elimination - HOMEWORK-es.srt 28.4 kB
  • 39 XGBoost/285 XGBoost in R-pt.srt 28.4 kB
  • 12 Logistic Regression/084 Logistic Regression Intuition-ja.srt 28.4 kB
  • 05 Multiple Linear Regression/051 Multiple Linear Regression in R - Backward Elimination - HOMEWORK-it.srt 28.4 kB
  • 09 Random Forest Regression/076 Random Forest Regression in Python-it.srt 28.3 kB
  • 21 K-Means Clustering/139 K-Means Clustering in Python-tr.srt 28.3 kB
  • 32 Convolutional Neural Networks/240 Step 1 - Convolution Operation-ja.srt 28.2 kB
  • 28 Thompson Sampling/180 Thompson Sampling Intuition-en.srt 28.2 kB
  • 27 Upper Confidence Bound (UCB)/174 Upper Confidence Bound in Python - Step 3-tr.srt 28.1 kB
  • 09 Random Forest Regression/077 Random Forest Regression in R-tr.srt 28.1 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/014 Missing Data-ja.srt 28.1 kB
  • 27 Upper Confidence Bound (UCB)/178 Upper Confidence Bound in R - Step 3-es.srt 28.0 kB
  • 28 Thompson Sampling/180 Thompson Sampling Intuition-tr.srt 28.0 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/210 Natural Language Processing in R - Step 10-tr.srt 27.9 kB
  • 27 Upper Confidence Bound (UCB)/173 Upper Confidence Bound in Python - Step 2-es.srt 27.9 kB
  • 21 K-Means Clustering/139 K-Means Clustering in Python-en.srt 27.8 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/015 Categorical Data-es.srt 27.8 kB
  • 31 Artificial Neural Networks/234 ANN in R - Step 1-tr.srt 27.8 kB
  • 15 Kernel SVM/111 Kernel SVM in Python-en.srt 27.8 kB
  • 09 Random Forest Regression/077 Random Forest Regression in R-en.srt 27.7 kB
  • 24 Apriori/157 Apriori Intuition-es.srt 27.7 kB
  • 27 Upper Confidence Bound (UCB)/178 Upper Confidence Bound in R - Step 3-pt.srt 27.7 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/017 Splitting the Dataset into the Training set and Test set-it.srt 27.6 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/015 Categorical Data-pt.srt 27.6 kB
  • 09 Random Forest Regression/076 Random Forest Regression in Python-tr.srt 27.6 kB
  • 27 Upper Confidence Bound (UCB)/174 Upper Confidence Bound in Python - Step 3-en.srt 27.6 kB
  • 13 K-Nearest Neighbors (K-NN)/101 K-NN in R-ja.srt 27.6 kB
  • 39 XGBoost/285 XGBoost in R-tr.srt 27.6 kB
  • 27 Upper Confidence Bound (UCB)/169 The Multi-Armed Bandit Problem-ja.srt 27.6 kB
  • 24 Apriori/162 Apriori in Python - Step 1-en.srt 27.5 kB
  • 24 Apriori/157 Apriori Intuition-pt.srt 27.5 kB
  • 27 Upper Confidence Bound (UCB)/178 Upper Confidence Bound in R - Step 3-it.srt 27.5 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/018 Feature Scaling-ja.srt 27.4 kB
  • 31 Artificial Neural Networks/234 ANN in R - Step 1-en.srt 27.4 kB
  • 35 Linear Discriminant Analysis (LDA)/270 LDA in Python-tr.srt 27.4 kB
  • 27 Upper Confidence Bound (UCB)/173 Upper Confidence Bound in Python - Step 2-pt.srt 27.4 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/015 Categorical Data-it.srt 27.3 kB
  • 38 Model Selection/279 Grid Search in Python - Step 1-ja.srt 27.3 kB
  • 24 Apriori/162 Apriori in Python - Step 1-tr.srt 27.3 kB
  • 27 Upper Confidence Bound (UCB)/172 Upper Confidence Bound in Python - Step 1-ja.srt 27.3 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/017 Splitting the Dataset into the Training set and Test set-tr.srt 27.2 kB
  • 05 Multiple Linear Regression/051 Multiple Linear Regression in R - Backward Elimination - HOMEWORK-tr.srt 27.2 kB
  • 21 K-Means Clustering/135 K-Means Clustering Intuition-ja.srt 27.2 kB
  • 08 Decision Tree Regression/072 Decision Tree Regression in Python-ja.srt 27.1 kB
  • 24 Apriori/157 Apriori Intuition-it.srt 27.1 kB
  • 35 Linear Discriminant Analysis (LDA)/270 LDA in Python-en.srt 27.1 kB
  • 05 Multiple Linear Regression/051 Multiple Linear Regression in R - Backward Elimination - HOMEWORK-en.srt 27.1 kB
  • 09 Random Forest Regression/076 Random Forest Regression in Python-en.srt 27.1 kB
  • 05 Multiple Linear Regression/040 Multiple Linear Regression Intuition - Step 5-ja.srt 27.0 kB
  • 27 Upper Confidence Bound (UCB)/173 Upper Confidence Bound in Python - Step 2-it.srt 27.0 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/201 Natural Language Processing in R - Step 1-es.srt 27.0 kB
  • 32 Convolutional Neural Networks/246 Softmax Cross-Entropy-es.srt 27.0 kB
  • 04 Simple Linear Regression/032 Simple Linear Regression in R - Step 4-ja.srt 26.9 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/210 Natural Language Processing in R - Step 10-en.srt 26.9 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/015 Categorical Data-tr.srt 26.9 kB
  • 32 Convolutional Neural Networks/246 Softmax Cross-Entropy-pt.srt 26.8 kB
  • 32 Convolutional Neural Networks/246 Softmax Cross-Entropy-it.srt 26.8 kB
  • 15 Kernel SVM/112 Kernel SVM in R-es.srt 26.7 kB
  • 32 Convolutional Neural Networks/239 What are convolutional neural networks-ja.srt 26.7 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/201 Natural Language Processing in R - Step 1-pt.srt 26.7 kB
  • 39 XGBoost/285 XGBoost in R-en.srt 26.6 kB
  • 24 Apriori/157 Apriori Intuition-tr.srt 26.6 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/015 Categorical Data-en.srt 26.5 kB
  • 24 Apriori/157 Apriori Intuition-en.srt 26.5 kB
  • 31 Artificial Neural Networks/215 The Neuron-pt.srt 26.5 kB
  • 15 Kernel SVM/112 Kernel SVM in R-pt.srt 26.5 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/017 Splitting the Dataset into the Training set and Test set-en.srt 26.5 kB
  • 24 Apriori/163 Apriori in Python - Step 2-ja.srt 26.4 kB
  • 32 Convolutional Neural Networks/246 Softmax Cross-Entropy-tr.srt 26.4 kB
  • 27 Upper Confidence Bound (UCB)/170 Upper Confidence Bound (UCB) Intuition-ja.srt 26.3 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/201 Natural Language Processing in R - Step 1-it.srt 26.3 kB
  • 24 Apriori/160 Apriori in R - Step 2-ja.srt 26.3 kB
  • 15 Kernel SVM/112 Kernel SVM in R-it.srt 26.3 kB
  • 27 Upper Confidence Bound (UCB)/178 Upper Confidence Bound in R - Step 3-tr.srt 26.3 kB
  • 36 Kernel PCA/273 Kernel PCA in Python-ja.srt 26.2 kB
  • 27 Upper Confidence Bound (UCB)/177 Upper Confidence Bound in R - Step 2-ja.srt 26.2 kB
  • 31 Artificial Neural Networks/215 The Neuron-es.srt 26.2 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/197 Natural Language Processing in Python - Step 8-es.srt 26.1 kB
  • 38 Model Selection/281 Grid Search in R-ja.srt 26.1 kB
  • 16 Naive Bayes/119 Naive Bayes in R-ja.srt 26.0 kB
  • 31 Artificial Neural Networks/215 The Neuron-it.srt 25.9 kB
  • 27 Upper Confidence Bound (UCB)/178 Upper Confidence Bound in R - Step 3-en.srt 25.9 kB
  • 16 Naive Bayes/114 Naive Bayes Intuition-ja.srt 25.9 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/197 Natural Language Processing in Python - Step 8-pt.srt 25.9 kB
  • 27 Upper Confidence Bound (UCB)/173 Upper Confidence Bound in Python - Step 2-tr.srt 25.9 kB
  • 32 Convolutional Neural Networks/246 Softmax Cross-Entropy-en.srt 25.9 kB
  • 15 Kernel SVM/112 Kernel SVM in R-tr.srt 25.9 kB
  • 27 Upper Confidence Bound (UCB)/173 Upper Confidence Bound in Python - Step 2-en.srt 25.9 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/197 Natural Language Processing in Python - Step 8-it.srt 25.8 kB
  • 32 Convolutional Neural Networks/242 Step 2 - Pooling-ja.srt 25.7 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/201 Natural Language Processing in R - Step 1-tr.srt 25.7 kB
  • 31 Artificial Neural Networks/215 The Neuron-en.srt 25.6 kB
  • 12 Logistic Regression/084 Logistic Regression Intuition-es.srt 25.6 kB
  • 12 Logistic Regression/084 Logistic Regression Intuition-pt.srt 25.6 kB
  • 05 Multiple Linear Regression/041 Multiple Linear Regression in Python - Step 1-es.srt 25.6 kB
  • 27 Upper Confidence Bound (UCB)/176 Upper Confidence Bound in R - Step 1-ja.srt 25.6 kB
  • 12 Logistic Regression/084 Logistic Regression Intuition-it.srt 25.5 kB
  • 31 Artificial Neural Networks/215 The Neuron-tr.srt 25.3 kB
  • 31 Artificial Neural Networks/224 ANN in Python - Step 1 - Installing Theano Tensorflow and Keras-ja.srt 25.3 kB
  • 05 Multiple Linear Regression/041 Multiple Linear Regression in Python - Step 1-pt.srt 25.3 kB
  • 32 Convolutional Neural Networks/240 Step 1 - Convolution Operation-es.srt 25.2 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/197 Natural Language Processing in Python - Step 8-tr.srt 25.1 kB
  • 15 Kernel SVM/112 Kernel SVM in R-en.srt 25.1 kB
  • 38 Model Selection/277 k-Fold Cross Validation in Python-ja.srt 25.0 kB
  • 05 Multiple Linear Regression/041 Multiple Linear Regression in Python - Step 1-it.srt 25.0 kB
  • 04 Simple Linear Regression/032 Simple Linear Regression in R - Step 4-es.srt 25.0 kB
  • 32 Convolutional Neural Networks/240 Step 1 - Convolution Operation-it.srt 24.9 kB
  • 04 Simple Linear Regression/032 Simple Linear Regression in R - Step 4-pt.srt 24.8 kB
  • 31 Artificial Neural Networks/237 ANN in R - Step 4 (Last step)-ja.srt 24.7 kB
  • 32 Convolutional Neural Networks/240 Step 1 - Convolution Operation-pt.srt 24.7 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/014 Missing Data-es.srt 24.6 kB
  • 13 K-Nearest Neighbors (K-NN)/100 K-NN in Python-ja.srt 24.6 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/201 Natural Language Processing in R - Step 1-en.srt 24.6 kB
  • 32 Convolutional Neural Networks/240 Step 1 - Convolution Operation-tr.srt 24.6 kB
  • 12 Logistic Regression/084 Logistic Regression Intuition-tr.srt 24.5 kB
  • 12 Logistic Regression/084 Logistic Regression Intuition-en.srt 24.5 kB
  • 08 Decision Tree Regression/072 Decision Tree Regression in Python-es.srt 24.5 kB
  • 08 Decision Tree Regression/072 Decision Tree Regression in Python-pt.srt 24.4 kB
  • 27 Upper Confidence Bound (UCB)/177 Upper Confidence Bound in R - Step 2-es.srt 24.4 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/018 Feature Scaling-pt.srt 24.4 kB
  • 13 K-Nearest Neighbors (K-NN)/101 K-NN in R-es.srt 24.4 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/197 Natural Language Processing in Python - Step 8-en.srt 24.4 kB
  • 05 Multiple Linear Regression/040 Multiple Linear Regression Intuition - Step 5-pt.srt 24.4 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/018 Feature Scaling-es.srt 24.3 kB
  • 04 Simple Linear Regression/028 Simple Linear Regression in Python - Step 4-ja.srt 24.3 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/014 Missing Data-pt.srt 24.3 kB
  • 08 Decision Tree Regression/072 Decision Tree Regression in Python-it.srt 24.3 kB
  • 21 K-Means Clustering/135 K-Means Clustering Intuition-pt.srt 24.3 kB
  • 05 Multiple Linear Regression/041 Multiple Linear Regression in Python - Step 1-tr.srt 24.2 kB
  • 04 Simple Linear Regression/032 Simple Linear Regression in R - Step 4-it.srt 24.1 kB
  • 21 K-Means Clustering/135 K-Means Clustering Intuition-es.srt 24.1 kB
  • 16 Naive Bayes/114 Naive Bayes Intuition-pt.srt 24.1 kB
  • 13 K-Nearest Neighbors (K-NN)/101 K-NN in R-pt.srt 24.1 kB
  • 27 Upper Confidence Bound (UCB)/177 Upper Confidence Bound in R - Step 2-pt.srt 24.0 kB
  • 05 Multiple Linear Regression/041 Multiple Linear Regression in Python - Step 1-en.srt 24.0 kB
  • 16 Naive Bayes/114 Naive Bayes Intuition-es.srt 24.0 kB
  • 38 Model Selection/279 Grid Search in Python - Step 1-es.srt 24.0 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/014 Missing Data-it.srt 23.9 kB
  • 27 Upper Confidence Bound (UCB)/172 Upper Confidence Bound in Python - Step 1-es.srt 23.9 kB
  • 38 Model Selection/279 Grid Search in Python - Step 1-pt.srt 23.9 kB
  • 05 Multiple Linear Regression/040 Multiple Linear Regression Intuition - Step 5-es.srt 23.8 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/018 Feature Scaling-it.srt 23.8 kB
  • 32 Convolutional Neural Networks/240 Step 1 - Convolution Operation-en.srt 23.8 kB
  • 34 Principal Component Analysis (PCA)/267 PCA in R - Step 3-ja.srt 23.8 kB
  • 21 K-Means Clustering/135 K-Means Clustering Intuition-it.srt 23.8 kB
  • 27 Upper Confidence Bound (UCB)/172 Upper Confidence Bound in Python - Step 1-pt.srt 23.8 kB
  • 13 K-Nearest Neighbors (K-NN)/101 K-NN in R-it.srt 23.8 kB
  • 27 Upper Confidence Bound (UCB)/177 Upper Confidence Bound in R - Step 2-it.srt 23.8 kB
  • 27 Upper Confidence Bound (UCB)/169 The Multi-Armed Bandit Problem-pt.srt 23.8 kB
  • 05 Multiple Linear Regression/040 Multiple Linear Regression Intuition - Step 5-it.srt 23.7 kB
  • 38 Model Selection/279 Grid Search in Python - Step 1-it.srt 23.7 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/018 Feature Scaling-tr.srt 23.7 kB
  • 21 K-Means Clustering/135 K-Means Clustering Intuition-tr.srt 23.7 kB
  • 36 Kernel PCA/273 Kernel PCA in Python-es.srt 23.7 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/014 Missing Data-tr.srt 23.6 kB
  • 13 K-Nearest Neighbors (K-NN)/101 K-NN in R-tr.srt 23.6 kB
  • 24 Apriori/160 Apriori in R - Step 2-es.srt 23.6 kB
  • 16 Naive Bayes/114 Naive Bayes Intuition-it.srt 23.6 kB
  • 04 Simple Linear Regression/028 Simple Linear Regression in Python - Step 4-es.srt 23.6 kB
  • 27 Upper Confidence Bound (UCB)/172 Upper Confidence Bound in Python - Step 1-it.srt 23.5 kB
  • 04 Simple Linear Regression/032 Simple Linear Regression in R - Step 4-en.srt 23.5 kB
  • 31 Artificial Neural Networks/218 How do Neural Networks learn-ja.srt 23.5 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/209 Natural Language Processing in R - Step 9-ja.srt 23.5 kB
  • 05 Multiple Linear Regression/040 Multiple Linear Regression Intuition - Step 5-tr.srt 23.5 kB
  • 08 Decision Tree Regression/072 Decision Tree Regression in Python-tr.srt 23.5 kB
  • 04 Simple Linear Regression/032 Simple Linear Regression in R - Step 4-tr.srt 23.5 kB
  • 36 Kernel PCA/273 Kernel PCA in Python-pt.srt 23.4 kB
  • 39 XGBoost/284 XGBoost in Python - Step 2-ja.srt 23.4 kB
  • 38 Model Selection/279 Grid Search in Python - Step 1-tr.srt 23.4 kB
  • 08 Decision Tree Regression/072 Decision Tree Regression in Python-en.srt 23.4 kB
  • 24 Apriori/160 Apriori in R - Step 2-pt.srt 23.4 kB
  • 24 Apriori/163 Apriori in Python - Step 2-es.srt 23.3 kB
  • 36 Kernel PCA/273 Kernel PCA in Python-it.srt 23.3 kB
  • 04 Simple Linear Regression/028 Simple Linear Regression in Python - Step 4-pt.srt 23.2 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/190 Natural Language Processing in Python - Step 1-ja.srt 23.2 kB
  • 05 Multiple Linear Regression/040 Multiple Linear Regression Intuition - Step 5-en.srt 23.2 kB
  • 24 Apriori/160 Apriori in R - Step 2-it.srt 23.2 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/014 Missing Data-en.srt 23.2 kB
  • 32 Convolutional Neural Networks/251 CNN in Python - Step 4-ja.srt 23.2 kB
  • 32 Convolutional Neural Networks/239 What are convolutional neural networks-es.srt 23.2 kB
  • 27 Upper Confidence Bound (UCB)/169 The Multi-Armed Bandit Problem-es.srt 23.2 kB
  • 21 K-Means Clustering/140 K-Means Clustering in R-ja.srt 23.1 kB
  • 31 Artificial Neural Networks/228 ANN in Python - Step 5-ja.srt 23.1 kB
  • 31 Artificial Neural Networks/217 How do Neural Networks work-ja.srt 23.1 kB
  • 27 Upper Confidence Bound (UCB)/169 The Multi-Armed Bandit Problem-it.srt 23.1 kB
  • 32 Convolutional Neural Networks/239 What are convolutional neural networks-pt.srt 23.1 kB
  • 27 Upper Confidence Bound (UCB)/170 Upper Confidence Bound (UCB) Intuition-pt.srt 23.1 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/018 Feature Scaling-en.srt 23.1 kB
  • 32 Convolutional Neural Networks/239 What are convolutional neural networks-it.srt 23.1 kB
  • 21 K-Means Clustering/135 K-Means Clustering Intuition-en.srt 23.0 kB
  • 16 Naive Bayes/114 Naive Bayes Intuition-en.srt 23.0 kB
  • 24 Apriori/163 Apriori in Python - Step 2-pt.srt 23.0 kB
  • 16 Naive Bayes/119 Naive Bayes in R-es.srt 23.0 kB
  • 38 Model Selection/281 Grid Search in R-es.srt 23.0 kB
  • 27 Upper Confidence Bound (UCB)/170 Upper Confidence Bound (UCB) Intuition-es.srt 23.0 kB
  • 31 Artificial Neural Networks/236 ANN in R - Step 3-ja.srt 23.0 kB
  • 16 Naive Bayes/114 Naive Bayes Intuition-tr.srt 23.0 kB
  • 27 Upper Confidence Bound (UCB)/172 Upper Confidence Bound in Python - Step 1-tr.srt 23.0 kB
  • 13 K-Nearest Neighbors (K-NN)/101 K-NN in R-en.srt 23.0 kB
  • 04 Simple Linear Regression/028 Simple Linear Regression in Python - Step 4-it.srt 23.0 kB
  • 24 Apriori/163 Apriori in Python - Step 2-it.srt 22.9 kB
  • 17 Decision Tree Classification/122 Decision Tree Classification in Python-ja.srt 22.9 kB
  • 05 Multiple Linear Regression/045 Multiple Linear Regression in Python - Backward Elimination - HOMEWORK-ja.srt 22.9 kB
  • 27 Upper Confidence Bound (UCB)/169 The Multi-Armed Bandit Problem-en.srt 22.8 kB
  • 27 Upper Confidence Bound (UCB)/170 Upper Confidence Bound (UCB) Intuition-it.srt 22.8 kB
  • 27 Upper Confidence Bound (UCB)/177 Upper Confidence Bound in R - Step 2-tr.srt 22.8 kB
  • 38 Model Selection/281 Grid Search in R-pt.srt 22.8 kB
  • 27 Upper Confidence Bound (UCB)/169 The Multi-Armed Bandit Problem-tr.srt 22.7 kB
  • 32 Convolutional Neural Networks/239 What are convolutional neural networks-tr.srt 22.7 kB
  • 36 Kernel PCA/273 Kernel PCA in Python-tr.srt 22.7 kB
  • 24 Apriori/160 Apriori in R - Step 2-en.srt 22.7 kB
  • 27 Upper Confidence Bound (UCB)/177 Upper Confidence Bound in R - Step 2-en.srt 22.7 kB
  • 31 Artificial Neural Networks/237 ANN in R - Step 4 (Last step)-es.srt 22.7 kB
  • 38 Model Selection/281 Grid Search in R-it.srt 22.7 kB
  • 24 Apriori/164 Apriori in Python - Step 3-ja.srt 22.7 kB
  • 38 Model Selection/277 k-Fold Cross Validation in Python-es.srt 22.6 kB
  • 16 Naive Bayes/119 Naive Bayes in R-pt.srt 22.6 kB
  • 32 Convolutional Neural Networks/248 CNN in Python - Step 1-ja.srt 22.6 kB
  • 32 Convolutional Neural Networks/239 What are convolutional neural networks-en.srt 22.6 kB
  • 27 Upper Confidence Bound (UCB)/170 Upper Confidence Bound (UCB) Intuition-tr.srt 22.6 kB
  • 38 Model Selection/279 Grid Search in Python - Step 1-en.srt 22.6 kB
  • 32 Convolutional Neural Networks/242 Step 2 - Pooling-es.srt 22.6 kB
  • 24 Apriori/160 Apriori in R - Step 2-tr.srt 22.5 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/012 Importing the Dataset-ja.srt 22.5 kB
  • 32 Convolutional Neural Networks/242 Step 2 - Pooling-pt.srt 22.5 kB
  • 16 Naive Bayes/119 Naive Bayes in R-tr.srt 22.5 kB
  • 27 Upper Confidence Bound (UCB)/170 Upper Confidence Bound (UCB) Intuition-en.srt 22.4 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/193 Natural Language Processing in Python - Step 4-ja.srt 22.4 kB
  • 27 Upper Confidence Bound (UCB)/172 Upper Confidence Bound in Python - Step 1-en.srt 22.4 kB
  • 14 Support Vector Machine (SVM)/104 SVM in Python-ja.srt 22.3 kB
  • 27 Upper Confidence Bound (UCB)/176 Upper Confidence Bound in R - Step 1-es.srt 22.3 kB
  • 16 Naive Bayes/119 Naive Bayes in R-it.srt 22.3 kB
  • 32 Convolutional Neural Networks/242 Step 2 - Pooling-it.srt 22.3 kB
  • 34 Principal Component Analysis (PCA)/265 PCA in R - Step 1-ja.srt 22.3 kB
  • 04 Simple Linear Regression/028 Simple Linear Regression in Python - Step 4-tr.srt 22.2 kB
  • 27 Upper Confidence Bound (UCB)/176 Upper Confidence Bound in R - Step 1-pt.srt 22.2 kB
  • 24 Apriori/163 Apriori in Python - Step 2-en.srt 22.2 kB
  • 31 Artificial Neural Networks/237 ANN in R - Step 4 (Last step)-pt.srt 22.2 kB
  • 31 Artificial Neural Networks/237 ANN in R - Step 4 (Last step)-it.srt 22.2 kB
  • 38 Model Selection/277 k-Fold Cross Validation in Python-pt.srt 22.2 kB
  • 38 Model Selection/277 k-Fold Cross Validation in Python-it.srt 22.2 kB
  • 04 Simple Linear Regression/028 Simple Linear Regression in Python - Step 4-en.srt 22.1 kB
  • 27 Upper Confidence Bound (UCB)/176 Upper Confidence Bound in R - Step 1-it.srt 22.1 kB
  • 30 -------------------- Part 8 Deep Learning --------------------/213 What is Deep Learning-ja.srt 22.0 kB
  • 36 Kernel PCA/273 Kernel PCA in Python-en.srt 22.0 kB
  • 24 Apriori/163 Apriori in Python - Step 2-tr.srt 22.0 kB
  • 38 Model Selection/281 Grid Search in R-tr.srt 21.9 kB
  • 13 K-Nearest Neighbors (K-NN)/100 K-NN in Python-es.srt 21.9 kB
  • 07 Support Vector Regression (SVR)/069 SVR in R-ja.srt 21.9 kB
  • 34 Principal Component Analysis (PCA)/267 PCA in R - Step 3-es.srt 21.9 kB
  • 31 Artificial Neural Networks/224 ANN in Python - Step 1 - Installing Theano Tensorflow and Keras-es.srt 21.8 kB
  • 13 K-Nearest Neighbors (K-NN)/100 K-NN in Python-pt.srt 21.8 kB
  • 31 Artificial Neural Networks/224 ANN in Python - Step 1 - Installing Theano Tensorflow and Keras-pt.srt 21.6 kB
  • 32 Convolutional Neural Networks/242 Step 2 - Pooling-tr.srt 21.6 kB
  • 34 Principal Component Analysis (PCA)/267 PCA in R - Step 3-pt.srt 21.6 kB
  • 13 K-Nearest Neighbors (K-NN)/100 K-NN in Python-it.srt 21.6 kB
  • 34 Principal Component Analysis (PCA)/267 PCA in R - Step 3-it.srt 21.6 kB
  • 38 Model Selection/277 k-Fold Cross Validation in Python-tr.srt 21.6 kB
  • 31 Artificial Neural Networks/237 ANN in R - Step 4 (Last step)-tr.srt 21.6 kB
  • 16 Naive Bayes/119 Naive Bayes in R-en.srt 21.5 kB
  • 32 Convolutional Neural Networks/242 Step 2 - Pooling-en.srt 21.5 kB
  • 34 Principal Component Analysis (PCA)/266 PCA in R - Step 2-ja.srt 21.5 kB
  • 32 Convolutional Neural Networks/251 CNN in Python - Step 4-es.srt 21.5 kB
  • 38 Model Selection/281 Grid Search in R-en.srt 21.4 kB
  • 27 Upper Confidence Bound (UCB)/176 Upper Confidence Bound in R - Step 1-tr.srt 21.4 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/209 Natural Language Processing in R - Step 9-es.srt 21.4 kB
  • 14 Support Vector Machine (SVM)/105 SVM in R-ja.srt 21.3 kB
  • 31 Artificial Neural Networks/224 ANN in Python - Step 1 - Installing Theano Tensorflow and Keras-it.srt 21.3 kB
  • 13 K-Nearest Neighbors (K-NN)/100 K-NN in Python-tr.srt 21.3 kB
  • 21 K-Means Clustering/137 K-Means Selecting The Number Of Clusters-ja.srt 21.2 kB
  • 31 Artificial Neural Networks/237 ANN in R - Step 4 (Last step)-en.srt 21.2 kB
  • 32 Convolutional Neural Networks/251 CNN in Python - Step 4-it.srt 21.2 kB
  • 06 Polynomial Regression/065 R Regression Template-ja.srt 21.1 kB
  • 27 Upper Confidence Bound (UCB)/176 Upper Confidence Bound in R - Step 1-en.srt 21.0 kB
  • 34 Principal Component Analysis (PCA)/262 PCA in Python - Step 1-ja.srt 21.0 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/209 Natural Language Processing in R - Step 9-pt.srt 20.9 kB
  • 31 Artificial Neural Networks/228 ANN in Python - Step 5-es.srt 20.9 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/209 Natural Language Processing in R - Step 9-it.srt 20.9 kB
  • 32 Convolutional Neural Networks/251 CNN in Python - Step 4-pt.srt 20.9 kB
  • 32 Convolutional Neural Networks/248 CNN in Python - Step 1-es.srt 20.9 kB
  • 34 Principal Component Analysis (PCA)/265 PCA in R - Step 1-es.srt 20.9 kB
  • 13 K-Nearest Neighbors (K-NN)/100 K-NN in Python-en.srt 20.9 kB
  • 31 Artificial Neural Networks/228 ANN in Python - Step 5-pt.srt 20.8 kB
  • 39 XGBoost/284 XGBoost in Python - Step 2-es.srt 20.8 kB
  • 31 Artificial Neural Networks/228 ANN in Python - Step 5-it.srt 20.8 kB
  • 34 Principal Component Analysis (PCA)/267 PCA in R - Step 3-tr.srt 20.7 kB
  • 38 Model Selection/277 k-Fold Cross Validation in Python-en.srt 20.7 kB
  • 31 Artificial Neural Networks/236 ANN in R - Step 3-es.srt 20.7 kB
  • 31 Artificial Neural Networks/224 ANN in Python - Step 1 - Installing Theano Tensorflow and Keras-tr.srt 20.6 kB
  • 05 Multiple Linear Regression/045 Multiple Linear Regression in Python - Backward Elimination - HOMEWORK-es.srt 20.6 kB
  • 39 XGBoost/284 XGBoost in Python - Step 2-pt.srt 20.6 kB
  • 21 K-Means Clustering/140 K-Means Clustering in R-es.srt 20.5 kB
  • 31 Artificial Neural Networks/236 ANN in R - Step 3-pt.srt 20.5 kB
  • 31 Artificial Neural Networks/224 ANN in Python - Step 1 - Installing Theano Tensorflow and Keras-en.srt 20.5 kB
  • 31 Artificial Neural Networks/236 ANN in R - Step 3-it.srt 20.5 kB
  • 05 Multiple Linear Regression/045 Multiple Linear Regression in Python - Backward Elimination - HOMEWORK-it.srt 20.4 kB
  • 39 XGBoost/284 XGBoost in Python - Step 2-it.srt 20.4 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/190 Natural Language Processing in Python - Step 1-es.srt 20.4 kB
  • 05 Multiple Linear Regression/045 Multiple Linear Regression in Python - Backward Elimination - HOMEWORK-pt.srt 20.4 kB
  • 34 Principal Component Analysis (PCA)/265 PCA in R - Step 1-pt.srt 20.4 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/209 Natural Language Processing in R - Step 9-tr.srt 20.4 kB
  • 24 Apriori/164 Apriori in Python - Step 3-es.srt 20.4 kB
  • 32 Convolutional Neural Networks/251 CNN in Python - Step 4-tr.srt 20.4 kB
  • 32 Convolutional Neural Networks/248 CNN in Python - Step 1-pt.srt 20.3 kB
  • 17 Decision Tree Classification/122 Decision Tree Classification in Python-es.srt 20.3 kB
  • 24 Apriori/164 Apriori in Python - Step 3-pt.srt 20.3 kB
  • 31 Artificial Neural Networks/218 How do Neural Networks learn-es.srt 20.3 kB
  • 17 Decision Tree Classification/122 Decision Tree Classification in Python-pt.srt 20.3 kB
  • 32 Convolutional Neural Networks/248 CNN in Python - Step 1-it.srt 20.3 kB
  • 34 Principal Component Analysis (PCA)/265 PCA in R - Step 1-it.srt 20.3 kB
  • 21 K-Means Clustering/140 K-Means Clustering in R-pt.srt 20.2 kB
  • 34 Principal Component Analysis (PCA)/267 PCA in R - Step 3-en.srt 20.2 kB
  • 31 Artificial Neural Networks/217 How do Neural Networks work-es.srt 20.2 kB
  • 31 Artificial Neural Networks/217 How do Neural Networks work-pt.srt 20.2 kB
  • 06 Polynomial Regression/056 Polynomial Regression in Python - Step 1-ja.srt 20.1 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/190 Natural Language Processing in Python - Step 1-pt.srt 20.1 kB
  • 31 Artificial Neural Networks/218 How do Neural Networks learn-pt.srt 20.1 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/209 Natural Language Processing in R - Step 9-en.srt 20.1 kB
  • 21 K-Means Clustering/140 K-Means Clustering in R-it.srt 20.1 kB
  • 31 Artificial Neural Networks/218 How do Neural Networks learn-it.srt 20.0 kB
  • 31 Artificial Neural Networks/217 How do Neural Networks work-it.srt 20.0 kB
  • 24 Apriori/164 Apriori in Python - Step 3-it.srt 20.0 kB
  • 31 Artificial Neural Networks/228 ANN in Python - Step 5-tr.srt 20.0 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/190 Natural Language Processing in Python - Step 1-it.srt 20.0 kB
  • 31 Artificial Neural Networks/228 ANN in Python - Step 5-en.srt 20.0 kB
  • 38 Model Selection/280 Grid Search in Python - Step 2-ja.srt 19.9 kB
  • 17 Decision Tree Classification/122 Decision Tree Classification in Python-it.srt 19.9 kB
  • 22 Hierarchical Clustering/143 Hierarchical Clustering Using Dendrograms-ja.srt 19.9 kB
  • 39 XGBoost/284 XGBoost in Python - Step 2-tr.srt 19.9 kB
  • 17 Decision Tree Classification/122 Decision Tree Classification in Python-tr.srt 19.9 kB
  • 15 Kernel SVM/108 The Kernel Trick-ja.srt 19.8 kB
  • 06 Polynomial Regression/057 Polynomial Regression in Python - Step 2-ja.srt 19.8 kB
  • 14 Support Vector Machine (SVM)/104 SVM in Python-es.srt 19.8 kB
  • 32 Convolutional Neural Networks/251 CNN in Python - Step 4-en.srt 19.7 kB
  • 31 Artificial Neural Networks/218 How do Neural Networks learn-tr.srt 19.7 kB
  • 05 Multiple Linear Regression/045 Multiple Linear Regression in Python - Backward Elimination - HOMEWORK-tr.srt 19.7 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/012 Importing the Dataset-es.srt 19.7 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/191 Natural Language Processing in Python - Step 2-ja.srt 19.7 kB
  • 14 Support Vector Machine (SVM)/104 SVM in Python-pt.srt 19.7 kB
  • 31 Artificial Neural Networks/236 ANN in R - Step 3-tr.srt 19.7 kB
  • 34 Principal Component Analysis (PCA)/262 PCA in Python - Step 1-es.srt 19.6 kB
  • 32 Convolutional Neural Networks/248 CNN in Python - Step 1-tr.srt 19.6 kB
  • 31 Artificial Neural Networks/217 How do Neural Networks work-en.srt 19.6 kB
  • 21 K-Means Clustering/140 K-Means Clustering in R-tr.srt 19.6 kB
  • 31 Artificial Neural Networks/217 How do Neural Networks work-tr.srt 19.5 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/190 Natural Language Processing in Python - Step 1-tr.srt 19.5 kB
  • 14 Support Vector Machine (SVM)/104 SVM in Python-it.srt 19.5 kB
  • 24 Apriori/164 Apriori in Python - Step 3-tr.srt 19.4 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/012 Importing the Dataset-pt.srt 19.4 kB
  • 31 Artificial Neural Networks/218 How do Neural Networks learn-en.srt 19.4 kB
  • 05 Multiple Linear Regression/045 Multiple Linear Regression in Python - Backward Elimination - HOMEWORK-en.srt 19.4 kB
  • 30 -------------------- Part 8 Deep Learning --------------------/213 What is Deep Learning-pt.srt 19.4 kB
  • 06 Polynomial Regression/065 R Regression Template-es.srt 19.4 kB
  • 07 Support Vector Regression (SVR)/069 SVR in R-es.srt 19.4 kB
  • 08 Decision Tree Regression/070 Decision Tree Regression Intuition-ja.srt 19.3 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/193 Natural Language Processing in Python - Step 4-es.srt 19.3 kB
  • 34 Principal Component Analysis (PCA)/265 PCA in R - Step 1-tr.srt 19.3 kB
  • 39 XGBoost/284 XGBoost in Python - Step 2-en.srt 19.3 kB
  • 31 Artificial Neural Networks/236 ANN in R - Step 3-en.srt 19.3 kB
  • 30 -------------------- Part 8 Deep Learning --------------------/213 What is Deep Learning-es.srt 19.3 kB
  • 14 Support Vector Machine (SVM)/104 SVM in Python-tr.srt 19.3 kB
  • 07 Support Vector Regression (SVR)/069 SVR in R-pt.srt 19.3 kB
  • 24 Apriori/164 Apriori in Python - Step 3-en.srt 19.3 kB
  • 34 Principal Component Analysis (PCA)/262 PCA in Python - Step 1-pt.srt 19.3 kB
  • 21 K-Means Clustering/140 K-Means Clustering in R-en.srt 19.2 kB
  • 34 Principal Component Analysis (PCA)/265 PCA in R - Step 1-en.srt 19.1 kB
  • 34 Principal Component Analysis (PCA)/262 PCA in Python - Step 1-it.srt 19.1 kB
  • 06 Polynomial Regression/065 R Regression Template-pt.srt 19.1 kB
  • 17 Decision Tree Classification/122 Decision Tree Classification in Python-en.srt 19.1 kB
  • 30 -------------------- Part 8 Deep Learning --------------------/213 What is Deep Learning-it.srt 19.1 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/193 Natural Language Processing in Python - Step 4-it.srt 19.1 kB
  • 14 Support Vector Machine (SVM)/105 SVM in R-es.srt 19.0 kB
  • 06 Polynomial Regression/065 R Regression Template-it.srt 19.0 kB
  • 34 Principal Component Analysis (PCA)/266 PCA in R - Step 2-es.srt 19.0 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/193 Natural Language Processing in Python - Step 4-pt.srt 19.0 kB
  • 07 Support Vector Regression (SVR)/069 SVR in R-it.srt 19.0 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/012 Importing the Dataset-it.srt 19.0 kB
  • 30 -------------------- Part 8 Deep Learning --------------------/213 What is Deep Learning-tr.srt 19.0 kB
  • 14 Support Vector Machine (SVM)/105 SVM in R-pt.srt 18.9 kB
  • 32 Convolutional Neural Networks/248 CNN in Python - Step 1-en.srt 18.8 kB
  • 14 Support Vector Machine (SVM)/104 SVM in Python-en.srt 18.8 kB
  • 21 K-Means Clustering/137 K-Means Selecting The Number Of Clusters-pt.srt 18.8 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/190 Natural Language Processing in Python - Step 1-en.srt 18.8 kB
  • 34 Principal Component Analysis (PCA)/266 PCA in R - Step 2-pt.srt 18.7 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/012 Importing the Dataset-tr.srt 18.7 kB
  • 07 Support Vector Regression (SVR)/069 SVR in R-tr.srt 18.6 kB
  • 21 K-Means Clustering/137 K-Means Selecting The Number Of Clusters-es.srt 18.6 kB
  • 06 Polynomial Regression/065 R Regression Template-tr.srt 18.6 kB
  • 06 Polynomial Regression/060 Python Regression Template-ja.srt 18.6 kB
  • 30 -------------------- Part 8 Deep Learning --------------------/213 What is Deep Learning-en.srt 18.6 kB
  • 25 Eclat/167 Eclat in R-ja.srt 18.5 kB
  • 14 Support Vector Machine (SVM)/105 SVM in R-it.srt 18.5 kB
  • 06 Polynomial Regression/056 Polynomial Regression in Python - Step 1-es.srt 18.5 kB
  • 14 Support Vector Machine (SVM)/105 SVM in R-tr.srt 18.4 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/193 Natural Language Processing in Python - Step 4-tr.srt 18.4 kB
  • 07 Support Vector Regression (SVR)/069 SVR in R-en.srt 18.4 kB
  • 06 Polynomial Regression/065 R Regression Template-en.srt 18.4 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/012 Importing the Dataset-en.srt 18.4 kB
  • 06 Polynomial Regression/056 Polynomial Regression in Python - Step 1-pt.srt 18.3 kB
  • 21 K-Means Clustering/137 K-Means Selecting The Number Of Clusters-it.srt 18.3 kB
  • 21 K-Means Clustering/137 K-Means Selecting The Number Of Clusters-en.srt 18.2 kB
  • 04 Simple Linear Regression/025 Simple Linear Regression in Python - Step 1-ja.srt 18.2 kB
  • 21 K-Means Clustering/137 K-Means Selecting The Number Of Clusters-tr.srt 18.2 kB
  • 34 Principal Component Analysis (PCA)/266 PCA in R - Step 2-it.srt 18.2 kB
  • 06 Polynomial Regression/056 Polynomial Regression in Python - Step 1-it.srt 18.1 kB
  • 34 Principal Component Analysis (PCA)/262 PCA in Python - Step 1-tr.srt 18.1 kB
  • 14 Support Vector Machine (SVM)/102 SVM Intuition-ja.srt 18.1 kB
  • 14 Support Vector Machine (SVM)/105 SVM in R-en.srt 18.1 kB
  • 34 Principal Component Analysis (PCA)/262 PCA in Python - Step 1-en.srt 18.1 kB
  • 06 Polynomial Regression/057 Polynomial Regression in Python - Step 2-es.srt 18.0 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/193 Natural Language Processing in Python - Step 4-en.srt 17.9 kB
  • 22 Hierarchical Clustering/143 Hierarchical Clustering Using Dendrograms-pt.srt 17.9 kB
  • 19 Evaluating Classification Models Performance/131 CAP Curve-ja.srt 17.9 kB
  • 16 Naive Bayes/116 Naive Bayes Intuition (Extras)-ja.srt 17.8 kB
  • 34 Principal Component Analysis (PCA)/266 PCA in R - Step 2-tr.srt 17.7 kB
  • 06 Polynomial Regression/057 Polynomial Regression in Python - Step 2-it.srt 17.7 kB
  • 06 Polynomial Regression/056 Polynomial Regression in Python - Step 1-tr.srt 17.7 kB
  • 15 Kernel SVM/108 The Kernel Trick-it.srt 17.7 kB
  • 22 Hierarchical Clustering/143 Hierarchical Clustering Using Dendrograms-it.srt 17.7 kB
  • 22 Hierarchical Clustering/143 Hierarchical Clustering Using Dendrograms-es.srt 17.6 kB
  • 06 Polynomial Regression/057 Polynomial Regression in Python - Step 2-pt.srt 17.6 kB
  • 06 Polynomial Regression/064 Polynomial Regression in R - Step 4-ja.srt 17.6 kB
  • 15 Kernel SVM/108 The Kernel Trick-es.srt 17.5 kB
  • 15 Kernel SVM/108 The Kernel Trick-pt.srt 17.5 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/199 Natural Language Processing in Python - Step 10-ja.srt 17.4 kB
  • 22 Hierarchical Clustering/143 Hierarchical Clustering Using Dendrograms-en.srt 17.4 kB
  • 08 Decision Tree Regression/070 Decision Tree Regression Intuition-pt.srt 17.3 kB
  • 22 Hierarchical Clustering/143 Hierarchical Clustering Using Dendrograms-tr.srt 17.3 kB
  • 08 Decision Tree Regression/070 Decision Tree Regression Intuition-it.srt 17.3 kB
  • 34 Principal Component Analysis (PCA)/264 PCA in Python - Step 3-ja.srt 17.3 kB
  • 34 Principal Component Analysis (PCA)/266 PCA in R - Step 2-en.srt 17.3 kB
  • 06 Polynomial Regression/056 Polynomial Regression in Python - Step 1-en.srt 17.3 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/019 And here is our Data Preprocessing Template-ja.srt 17.2 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/191 Natural Language Processing in Python - Step 2-es.srt 17.2 kB
  • 06 Polynomial Regression/060 Python Regression Template-es.srt 17.2 kB
  • 38 Model Selection/280 Grid Search in Python - Step 2-es.srt 17.2 kB
  • 08 Decision Tree Regression/070 Decision Tree Regression Intuition-es.srt 17.1 kB
  • 06 Polynomial Regression/057 Polynomial Regression in Python - Step 2-tr.srt 17.1 kB
  • 22 Hierarchical Clustering/142 Hierarchical Clustering How Dendrograms Work-ja.srt 17.1 kB
  • 06 Polynomial Regression/060 Python Regression Template-pt.srt 17.1 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/191 Natural Language Processing in Python - Step 2-pt.srt 17.0 kB
  • 38 Model Selection/280 Grid Search in Python - Step 2-pt.srt 17.0 kB
  • 05 Multiple Linear Regression/044 Multiple Linear Regression in Python - Backward Elimination - Preparation-ja.srt 17.0 kB
  • 06 Polynomial Regression/062 Polynomial Regression in R - Step 2-ja.srt 17.0 kB
  • 06 Polynomial Regression/057 Polynomial Regression in Python - Step 2-en.srt 16.9 kB
  • 38 Model Selection/280 Grid Search in Python - Step 2-it.srt 16.9 kB
  • 15 Kernel SVM/108 The Kernel Trick-en.srt 16.9 kB
  • 15 Kernel SVM/108 The Kernel Trick-tr.srt 16.9 kB
  • 22 Hierarchical Clustering/141 Hierarchical Clustering Intuition-ja.srt 16.9 kB
  • 06 Polynomial Regression/060 Python Regression Template-it.srt 16.8 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/191 Natural Language Processing in Python - Step 2-it.srt 16.8 kB
  • 39 XGBoost/283 XGBoost in Python - Step 1-ja.srt 16.8 kB
  • 08 Decision Tree Regression/070 Decision Tree Regression Intuition-en.srt 16.8 kB
  • 31 Artificial Neural Networks/219 Gradient Descent-ja.srt 16.8 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/191 Natural Language Processing in Python - Step 2-tr.srt 16.7 kB
  • 10 Evaluating Regression Models Performance/079 Adjusted R-Squared Intuition-ja.srt 16.7 kB
  • 08 Decision Tree Regression/070 Decision Tree Regression Intuition-tr.srt 16.6 kB
  • 05 Multiple Linear Regression/046 Multiple Linear Regression in Python - Backward Elimination - Homework Solution-ja.srt 16.6 kB
  • 06 Polynomial Regression/060 Python Regression Template-tr.srt 16.5 kB
  • 04 Simple Linear Regression/025 Simple Linear Regression in Python - Step 1-es.srt 16.5 kB
  • 16 Naive Bayes/118 Naive Bayes in Python-ja.srt 16.5 kB
  • 16 Naive Bayes/116 Naive Bayes Intuition (Extras)-pt.srt 16.4 kB
  • 19 Evaluating Classification Models Performance/131 CAP Curve-es.srt 16.4 kB
  • 19 Evaluating Classification Models Performance/131 CAP Curve-it.srt 16.3 kB
  • 19 Evaluating Classification Models Performance/131 CAP Curve-pt.srt 16.3 kB
  • 16 Naive Bayes/116 Naive Bayes Intuition (Extras)-es.srt 16.3 kB
  • 25 Eclat/167 Eclat in R-es.srt 16.3 kB
  • 05 Multiple Linear Regression/044 Multiple Linear Regression in Python - Backward Elimination - Preparation-es.srt 16.3 kB
  • 05 Multiple Linear Regression/044 Multiple Linear Regression in Python - Backward Elimination - Preparation-it.srt 16.3 kB
  • 38 Model Selection/280 Grid Search in Python - Step 2-tr.srt 16.3 kB
  • 04 Simple Linear Regression/025 Simple Linear Regression in Python - Step 1-pt.srt 16.2 kB
  • 34 Principal Component Analysis (PCA)/264 PCA in Python - Step 3-es.srt 16.2 kB
  • 05 Multiple Linear Regression/049 Multiple Linear Regression in R - Step 2-ja.srt 16.2 kB
  • 05 Multiple Linear Regression/044 Multiple Linear Regression in Python - Backward Elimination - Preparation-pt.srt 16.2 kB
  • 19 Evaluating Classification Models Performance/131 CAP Curve-tr.srt 16.2 kB
  • 25 Eclat/167 Eclat in R-pt.srt 16.2 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/191 Natural Language Processing in Python - Step 2-en.srt 16.2 kB
  • 16 Naive Bayes/116 Naive Bayes Intuition (Extras)-it.srt 16.2 kB
  • 06 Polynomial Regression/060 Python Regression Template-en.srt 16.1 kB
  • 14 Support Vector Machine (SVM)/102 SVM Intuition-pt.srt 16.1 kB
  • 06 Polynomial Regression/061 Polynomial Regression in R - Step 1-ja.srt 16.1 kB
  • 25 Eclat/167 Eclat in R-it.srt 16.1 kB
  • 06 Polynomial Regression/062 Polynomial Regression in R - Step 2-pt.srt 16.1 kB
  • 04 Simple Linear Regression/025 Simple Linear Regression in Python - Step 1-it.srt 16.1 kB
  • 06 Polynomial Regression/062 Polynomial Regression in R - Step 2-es.srt 16.0 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/202 Natural Language Processing in R - Step 2-ja.srt 16.0 kB
  • 19 Evaluating Classification Models Performance/131 CAP Curve-en.srt 16.0 kB
  • 14 Support Vector Machine (SVM)/102 SVM Intuition-es.srt 15.9 kB
  • 14 Support Vector Machine (SVM)/102 SVM Intuition-it.srt 15.9 kB
  • 34 Principal Component Analysis (PCA)/264 PCA in Python - Step 3-pt.srt 15.9 kB
  • 06 Polynomial Regression/062 Polynomial Regression in R - Step 2-it.srt 15.9 kB
  • 05 Multiple Linear Regression/049 Multiple Linear Regression in R - Step 2-es.srt 15.8 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/199 Natural Language Processing in Python - Step 10-es.srt 15.8 kB
  • 34 Principal Component Analysis (PCA)/264 PCA in Python - Step 3-it.srt 15.8 kB
  • 06 Polynomial Regression/064 Polynomial Regression in R - Step 4-it.srt 15.8 kB
  • 32 Convolutional Neural Networks/257 CNN in Python - Step 10-ja.srt 15.8 kB
  • 05 Multiple Linear Regression/049 Multiple Linear Regression in R - Step 2-pt.srt 15.7 kB
  • 16 Naive Bayes/116 Naive Bayes Intuition (Extras)-en.srt 15.7 kB
  • 06 Polynomial Regression/064 Polynomial Regression in R - Step 4-es.srt 15.7 kB
  • 38 Model Selection/280 Grid Search in Python - Step 2-en.srt 15.7 kB
  • 16 Naive Bayes/116 Naive Bayes Intuition (Extras)-tr.srt 15.7 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/199 Natural Language Processing in Python - Step 10-it.srt 15.6 kB
  • 05 Multiple Linear Regression/049 Multiple Linear Regression in R - Step 2-it.srt 15.6 kB
  • 06 Polynomial Regression/064 Polynomial Regression in R - Step 4-pt.srt 15.6 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/199 Natural Language Processing in Python - Step 10-pt.srt 15.6 kB
  • 25 Eclat/167 Eclat in R-en.srt 15.6 kB
  • 14 Support Vector Machine (SVM)/102 SVM Intuition-en.srt 15.5 kB
  • 14 Support Vector Machine (SVM)/102 SVM Intuition-tr.srt 15.5 kB
  • 05 Multiple Linear Regression/044 Multiple Linear Regression in Python - Backward Elimination - Preparation-tr.srt 15.5 kB
  • 25 Eclat/167 Eclat in R-tr.srt 15.5 kB
  • 04 Simple Linear Regression/025 Simple Linear Regression in Python - Step 1-tr.srt 15.4 kB
  • 01 Welcome to the course/005 Installing Python and Anaconda (Mac Linux Windows)-ja.srt 15.4 kB
  • 06 Polynomial Regression/062 Polynomial Regression in R - Step 2-tr.srt 15.3 kB
  • 05 Multiple Linear Regression/044 Multiple Linear Regression in Python - Backward Elimination - Preparation-en.srt 15.3 kB
  • 04 Simple Linear Regression/025 Simple Linear Regression in Python - Step 1-en.srt 15.3 kB
  • 34 Principal Component Analysis (PCA)/264 PCA in Python - Step 3-tr.srt 15.2 kB
  • 05 Multiple Linear Regression/049 Multiple Linear Regression in R - Step 2-en.srt 15.2 kB
  • 06 Polynomial Regression/064 Polynomial Regression in R - Step 4-en.srt 15.2 kB
  • 34 Principal Component Analysis (PCA)/264 PCA in Python - Step 3-en.srt 15.2 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/199 Natural Language Processing in Python - Step 10-tr.srt 15.1 kB
  • 06 Polynomial Regression/064 Polynomial Regression in R - Step 4-tr.srt 15.1 kB
  • 06 Polynomial Regression/062 Polynomial Regression in R - Step 2-en.srt 15.1 kB
  • 10 Evaluating Regression Models Performance/079 Adjusted R-Squared Intuition-pt.srt 15.0 kB
  • 06 Polynomial Regression/061 Polynomial Regression in R - Step 1-es.srt 15.0 kB
  • 05 Multiple Linear Regression/049 Multiple Linear Regression in R - Step 2-tr.srt 14.9 kB
  • 06 Polynomial Regression/061 Polynomial Regression in R - Step 1-pt.srt 14.9 kB
  • 10 Evaluating Regression Models Performance/079 Adjusted R-Squared Intuition-it.srt 14.9 kB
  • 06 Polynomial Regression/061 Polynomial Regression in R - Step 1-it.srt 14.9 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/019 And here is our Data Preprocessing Template-es.srt 14.9 kB
  • 31 Artificial Neural Networks/219 Gradient Descent-es.srt 14.9 kB
  • 39 XGBoost/283 XGBoost in Python - Step 1-es.srt 14.8 kB
  • 31 Artificial Neural Networks/219 Gradient Descent-pt.srt 14.8 kB
  • 05 Multiple Linear Regression/046 Multiple Linear Regression in Python - Backward Elimination - Homework Solution-it.srt 14.8 kB
  • 39 XGBoost/283 XGBoost in Python - Step 1-pt.srt 14.8 kB
  • 21 K-Means Clustering/136 K-Means Random Initialization Trap-ja.srt 14.8 kB
  • 04 Simple Linear Regression/026 Simple Linear Regression in Python - Step 2-ja.srt 14.8 kB
  • 05 Multiple Linear Regression/046 Multiple Linear Regression in Python - Backward Elimination - Homework Solution-es.srt 14.8 kB
  • 10 Evaluating Regression Models Performance/081 Interpreting Linear Regression Coefficients-ja.srt 14.7 kB
  • 10 Evaluating Regression Models Performance/079 Adjusted R-Squared Intuition-es.srt 14.7 kB
  • 31 Artificial Neural Networks/219 Gradient Descent-it.srt 14.7 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/199 Natural Language Processing in Python - Step 10-en.srt 14.7 kB
  • 10 Evaluating Regression Models Performance/080 Evaluating Regression Models Performance - Homeworks Final Part-ja.srt 14.7 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/019 And here is our Data Preprocessing Template-pt.srt 14.7 kB
  • 22 Hierarchical Clustering/141 Hierarchical Clustering Intuition-pt.srt 14.7 kB
  • 05 Multiple Linear Regression/046 Multiple Linear Regression in Python - Backward Elimination - Homework Solution-pt.srt 14.7 kB
  • 22 Hierarchical Clustering/142 Hierarchical Clustering How Dendrograms Work-es.srt 14.7 kB
  • 22 Hierarchical Clustering/141 Hierarchical Clustering Intuition-es.srt 14.6 kB
  • 17 Decision Tree Classification/120 Decision Tree Classification Intuition-ja.srt 14.6 kB
  • 22 Hierarchical Clustering/142 Hierarchical Clustering How Dendrograms Work-pt.srt 14.6 kB
  • 39 XGBoost/283 XGBoost in Python - Step 1-it.srt 14.6 kB
  • 31 Artificial Neural Networks/220 Stochastic Gradient Descent-ja.srt 14.5 kB
  • 16 Naive Bayes/118 Naive Bayes in Python-es.srt 14.4 kB
  • 31 Artificial Neural Networks/219 Gradient Descent-tr.srt 14.4 kB
  • 22 Hierarchical Clustering/142 Hierarchical Clustering How Dendrograms Work-it.srt 14.4 kB
  • 22 Hierarchical Clustering/141 Hierarchical Clustering Intuition-en.srt 14.4 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/019 And here is our Data Preprocessing Template-it.srt 14.4 kB
  • 22 Hierarchical Clustering/141 Hierarchical Clustering Intuition-it.srt 14.4 kB
  • 10 Evaluating Regression Models Performance/079 Adjusted R-Squared Intuition-tr.srt 14.4 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/019 And here is our Data Preprocessing Template-tr.srt 14.4 kB
  • 31 Artificial Neural Networks/219 Gradient Descent-en.srt 14.4 kB
  • 22 Hierarchical Clustering/141 Hierarchical Clustering Intuition-tr.srt 14.4 kB
  • 05 Multiple Linear Regression/046 Multiple Linear Regression in Python - Backward Elimination - Homework Solution-tr.srt 14.3 kB
  • 31 Artificial Neural Networks/216 The Activation Function-ja.srt 14.3 kB
  • 16 Naive Bayes/118 Naive Bayes in Python-pt.srt 14.3 kB
  • 10 Evaluating Regression Models Performance/079 Adjusted R-Squared Intuition-en.srt 14.3 kB
  • 39 XGBoost/283 XGBoost in Python - Step 1-tr.srt 14.2 kB
  • 32 Convolutional Neural Networks/257 CNN in Python - Step 10-es.srt 14.2 kB
  • 22 Hierarchical Clustering/142 Hierarchical Clustering How Dendrograms Work-en.srt 14.1 kB
  • 06 Polynomial Regression/061 Polynomial Regression in R - Step 1-tr.srt 14.1 kB
  • 16 Naive Bayes/118 Naive Bayes in Python-it.srt 14.1 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/202 Natural Language Processing in R - Step 2-es.srt 14.1 kB
  • 32 Convolutional Neural Networks/257 CNN in Python - Step 10-it.srt 14.1 kB
  • 05 Multiple Linear Regression/048 Multiple Linear Regression in R - Step 1-ja.srt 14.0 kB
  • 16 Naive Bayes/118 Naive Bayes in Python-tr.srt 14.0 kB
  • 39 XGBoost/283 XGBoost in Python - Step 1-en.srt 14.0 kB
  • 05 Multiple Linear Regression/046 Multiple Linear Regression in Python - Backward Elimination - Homework Solution-en.srt 14.0 kB
  • 34 Principal Component Analysis (PCA)/263 PCA in Python - Step 2-ja.srt 14.0 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/019 And here is our Data Preprocessing Template-en.srt 13.9 kB
  • 10 Evaluating Regression Models Performance/081 Interpreting Linear Regression Coefficients-it.srt 13.9 kB
  • 06 Polynomial Regression/061 Polynomial Regression in R - Step 1-en.srt 13.9 kB
  • 22 Hierarchical Clustering/142 Hierarchical Clustering How Dendrograms Work-tr.srt 13.9 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/202 Natural Language Processing in R - Step 2-pt.srt 13.9 kB
  • 05 Multiple Linear Regression/052 Multiple Linear Regression in R - Backward Elimination - Homework Solution-ja.srt 13.9 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/202 Natural Language Processing in R - Step 2-it.srt 13.9 kB
  • 32 Convolutional Neural Networks/257 CNN in Python - Step 10-pt.srt 13.9 kB
  • 10 Evaluating Regression Models Performance/081 Interpreting Linear Regression Coefficients-pt.srt 13.7 kB
  • 10 Evaluating Regression Models Performance/081 Interpreting Linear Regression Coefficients-es.srt 13.6 kB
  • 32 Convolutional Neural Networks/257 CNN in Python - Step 10-tr.srt 13.6 kB
  • 16 Naive Bayes/118 Naive Bayes in Python-en.srt 13.5 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/010 Get the dataset-ja.srt 13.4 kB
  • 31 Artificial Neural Networks/231 ANN in Python - Step 8-ja.srt 13.4 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/202 Natural Language Processing in R - Step 2-tr.srt 13.3 kB
  • 32 Convolutional Neural Networks/257 CNN in Python - Step 10-en.srt 13.3 kB
  • 17 Decision Tree Classification/120 Decision Tree Classification Intuition-es.srt 13.3 kB
  • 10 Evaluating Regression Models Performance/081 Interpreting Linear Regression Coefficients-tr.srt 13.3 kB
  • 17 Decision Tree Classification/120 Decision Tree Classification Intuition-pt.srt 13.3 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/194 Natural Language Processing in Python - Step 5-ja.srt 13.2 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/202 Natural Language Processing in R - Step 2-en.srt 13.2 kB
  • 28 Thompson Sampling/181 Algorithm Comparison UCB vs Thompson Sampling-ja.srt 13.2 kB
  • 10 Evaluating Regression Models Performance/081 Interpreting Linear Regression Coefficients-en.srt 13.2 kB
  • 31 Artificial Neural Networks/220 Stochastic Gradient Descent-es.srt 13.1 kB
  • 21 K-Means Clustering/136 K-Means Random Initialization Trap-es.srt 13.1 kB
  • 34 Principal Component Analysis (PCA)/263 PCA in Python - Step 2-es.srt 13.1 kB
  • 21 K-Means Clustering/136 K-Means Random Initialization Trap-pt.srt 13.1 kB
  • 31 Artificial Neural Networks/220 Stochastic Gradient Descent-pt.srt 13.0 kB
  • 10 Evaluating Regression Models Performance/080 Evaluating Regression Models Performance - Homeworks Final Part-pt.srt 13.0 kB
  • 21 K-Means Clustering/136 K-Means Random Initialization Trap-it.srt 13.0 kB
  • 34 Principal Component Analysis (PCA)/263 PCA in Python - Step 2-pt.srt 12.9 kB
  • 04 Simple Linear Regression/026 Simple Linear Regression in Python - Step 2-es.srt 12.9 kB
  • 17 Decision Tree Classification/120 Decision Tree Classification Intuition-tr.srt 12.9 kB
  • 10 Evaluating Regression Models Performance/080 Evaluating Regression Models Performance - Homeworks Final Part-it.srt 12.9 kB
  • 10 Evaluating Regression Models Performance/080 Evaluating Regression Models Performance - Homeworks Final Part-es.srt 12.9 kB
  • 04 Simple Linear Regression/026 Simple Linear Regression in Python - Step 2-it.srt 12.9 kB
  • 17 Decision Tree Classification/120 Decision Tree Classification Intuition-it.srt 12.9 kB
  • 31 Artificial Neural Networks/220 Stochastic Gradient Descent-it.srt 12.9 kB
  • 31 Artificial Neural Networks/235 ANN in R - Step 2-ja.srt 12.8 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/203 Natural Language Processing in R - Step 3-ja.srt 12.8 kB
  • 04 Simple Linear Regression/026 Simple Linear Regression in Python - Step 2-pt.srt 12.8 kB
  • 10 Evaluating Regression Models Performance/080 Evaluating Regression Models Performance - Homeworks Final Part-tr.srt 12.8 kB
  • 19 Evaluating Classification Models Performance/128 False Positives False Negatives-ja.srt 12.8 kB
  • 21 K-Means Clustering/136 K-Means Random Initialization Trap-en.srt 12.8 kB
  • 10 Evaluating Regression Models Performance/080 Evaluating Regression Models Performance - Homeworks Final Part-en.srt 12.7 kB
  • 17 Decision Tree Classification/120 Decision Tree Classification Intuition-en.srt 12.7 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/196 Natural Language Processing in Python - Step 7-ja.srt 12.7 kB
  • 31 Artificial Neural Networks/216 The Activation Function-pt.srt 12.6 kB
  • 01 Welcome to the course/005 Installing Python and Anaconda (Mac Linux Windows)-pt.srt 12.6 kB
  • 21 K-Means Clustering/136 K-Means Random Initialization Trap-tr.srt 12.6 kB
  • 34 Principal Component Analysis (PCA)/263 PCA in Python - Step 2-it.srt 12.6 kB
  • 31 Artificial Neural Networks/216 The Activation Function-es.srt 12.6 kB
  • 15 Kernel SVM/107 Mapping to a higher dimension-ja.srt 12.6 kB
  • 31 Artificial Neural Networks/220 Stochastic Gradient Descent-tr.srt 12.5 kB
  • 31 Artificial Neural Networks/216 The Activation Function-it.srt 12.5 kB
  • 01 Welcome to the course/005 Installing Python and Anaconda (Mac Linux Windows)-es.srt 12.5 kB
  • 05 Multiple Linear Regression/052 Multiple Linear Regression in R - Backward Elimination - Homework Solution-es.srt 12.4 kB
  • 31 Artificial Neural Networks/220 Stochastic Gradient Descent-en.srt 12.4 kB
  • 05 Multiple Linear Regression/048 Multiple Linear Regression in R - Step 1-es.srt 12.4 kB
  • 01 Welcome to the course/005 Installing Python and Anaconda (Mac Linux Windows)-tr.srt 12.4 kB
  • 31 Artificial Neural Networks/233 ANN in Python - Step 10-ja.srt 12.4 kB
  • 05 Multiple Linear Regression/052 Multiple Linear Regression in R - Backward Elimination - Homework Solution-it.srt 12.4 kB
  • 05 Multiple Linear Regression/052 Multiple Linear Regression in R - Backward Elimination - Homework Solution-pt.srt 12.4 kB
  • 31 Artificial Neural Networks/216 The Activation Function-en.srt 12.3 kB
  • 01 Welcome to the course/005 Installing Python and Anaconda (Mac Linux Windows)-it.srt 12.3 kB
  • 05 Multiple Linear Regression/037 Multiple Linear Regression Intuition - Step 3-ja.srt 12.3 kB
  • 05 Multiple Linear Regression/052 Multiple Linear Regression in R - Backward Elimination - Homework Solution-tr.srt 12.3 kB
  • 04 Simple Linear Regression/026 Simple Linear Regression in Python - Step 2-tr.srt 12.3 kB
  • 31 Artificial Neural Networks/216 The Activation Function-tr.srt 12.2 kB
  • 05 Multiple Linear Regression/048 Multiple Linear Regression in R - Step 1-pt.srt 12.2 kB
  • 04 Simple Linear Regression/026 Simple Linear Regression in Python - Step 2-en.srt 12.2 kB
  • 34 Principal Component Analysis (PCA)/263 PCA in Python - Step 2-tr.srt 12.1 kB
  • 01 Welcome to the course/005 Installing Python and Anaconda (Mac Linux Windows)-en.srt 12.1 kB
  • 34 Principal Component Analysis (PCA)/263 PCA in Python - Step 2-en.srt 12.1 kB
  • 05 Multiple Linear Regression/048 Multiple Linear Regression in R - Step 1-it.srt 12.1 kB
  • 31 Artificial Neural Networks/231 ANN in Python - Step 8-es.srt 11.9 kB
  • 31 Artificial Neural Networks/231 ANN in Python - Step 8-pt.srt 11.8 kB
  • 19 Evaluating Classification Models Performance/128 False Positives False Negatives-es.srt 11.7 kB
  • 19 Evaluating Classification Models Performance/128 False Positives False Negatives-it.srt 11.7 kB
  • 05 Multiple Linear Regression/052 Multiple Linear Regression in R - Backward Elimination - Homework Solution-en.srt 11.7 kB
  • 01 Welcome to the course/007 Installing R and R Studio (Mac Linux Windows)-ja.srt 11.7 kB
  • 05 Multiple Linear Regression/048 Multiple Linear Regression in R - Step 1-tr.srt 11.6 kB
  • 05 Multiple Linear Regression/048 Multiple Linear Regression in R - Step 1-en.srt 11.6 kB
  • 07 Support Vector Regression (SVR)/067 SVR Intuition-en.srt 11.6 kB
  • 28 Thompson Sampling/181 Algorithm Comparison UCB vs Thompson Sampling-pt.srt 11.6 kB
  • 31 Artificial Neural Networks/231 ANN in Python - Step 8-it.srt 11.6 kB
  • 31 Artificial Neural Networks/232 ANN in Python - Step 9-ja.srt 11.6 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/010 Get the dataset-es.srt 11.5 kB
  • 19 Evaluating Classification Models Performance/128 False Positives False Negatives-pt.srt 11.5 kB
  • 28 Thompson Sampling/181 Algorithm Comparison UCB vs Thompson Sampling-es.srt 11.5 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/194 Natural Language Processing in Python - Step 5-es.srt 11.5 kB
  • 09 Random Forest Regression/074 Random Forest Regression Intuition-ja.srt 11.5 kB
  • 15 Kernel SVM/107 Mapping to a higher dimension-pt.srt 11.5 kB
  • 28 Thompson Sampling/181 Algorithm Comparison UCB vs Thompson Sampling-it.srt 11.4 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/010 Get the dataset-pt.srt 11.4 kB
  • 28 Thompson Sampling/181 Algorithm Comparison UCB vs Thompson Sampling-en.srt 11.4 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/194 Natural Language Processing in Python - Step 5-it.srt 11.4 kB
  • 28 Thompson Sampling/181 Algorithm Comparison UCB vs Thompson Sampling-tr.srt 11.3 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/194 Natural Language Processing in Python - Step 5-tr.srt 11.3 kB
  • 31 Artificial Neural Networks/231 ANN in Python - Step 8-en.srt 11.3 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/194 Natural Language Processing in Python - Step 5-pt.srt 11.3 kB
  • 15 Kernel SVM/107 Mapping to a higher dimension-es.srt 11.3 kB
  • 31 Artificial Neural Networks/233 ANN in Python - Step 10-es.srt 11.3 kB
  • 15 Kernel SVM/107 Mapping to a higher dimension-it.srt 11.2 kB
  • 15 Kernel SVM/107 Mapping to a higher dimension-tr.srt 11.2 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/010 Get the dataset-it.srt 11.2 kB
  • 05 Multiple Linear Regression/037 Multiple Linear Regression Intuition - Step 3-pt.srt 11.2 kB
  • 19 Evaluating Classification Models Performance/128 False Positives False Negatives-en.srt 11.1 kB
  • 32 Convolutional Neural Networks/254 CNN in Python - Step 7-ja.srt 11.1 kB
  • 31 Artificial Neural Networks/233 ANN in Python - Step 10-it.srt 11.1 kB
  • 31 Artificial Neural Networks/235 ANN in R - Step 2-es.srt 11.1 kB
  • 31 Artificial Neural Networks/231 ANN in Python - Step 8-tr.srt 11.1 kB
  • 19 Evaluating Classification Models Performance/128 False Positives False Negatives-tr.srt 11.1 kB
  • 31 Artificial Neural Networks/235 ANN in R - Step 2-pt.srt 11.1 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/203 Natural Language Processing in R - Step 3-es.srt 11.1 kB
  • 05 Multiple Linear Regression/037 Multiple Linear Regression Intuition - Step 3-it.srt 11.0 kB
  • 22 Hierarchical Clustering/146 HC in Python - Step 2-ja.srt 11.0 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/196 Natural Language Processing in Python - Step 7-es.srt 11.0 kB
  • 04 Simple Linear Regression/027 Simple Linear Regression in Python - Step 3-ja.srt 11.0 kB
  • 05 Multiple Linear Regression/037 Multiple Linear Regression Intuition - Step 3-es.srt 11.0 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/203 Natural Language Processing in R - Step 3-pt.srt 11.0 kB
  • 31 Artificial Neural Networks/233 ANN in Python - Step 10-pt.srt 11.0 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/010 Get the dataset-tr.srt 11.0 kB
  • 32 Convolutional Neural Networks/241 Step 1(b) - ReLU Layer-ja.srt 11.0 kB
  • 01 Welcome to the course/002 Why Machine Learning is the Future-ja.srt 11.0 kB
  • 31 Artificial Neural Networks/233 ANN in Python - Step 10-tr.srt 10.9 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/010 Get the dataset-en.srt 10.9 kB
  • 31 Artificial Neural Networks/235 ANN in R - Step 2-it.srt 10.9 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/203 Natural Language Processing in R - Step 3-it.srt 10.9 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/194 Natural Language Processing in Python - Step 5-en.srt 10.9 kB
  • 12 Logistic Regression/092 Logistic Regression in R - Step 1-ja.srt 10.9 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/196 Natural Language Processing in Python - Step 7-pt.srt 10.9 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/196 Natural Language Processing in Python - Step 7-it.srt 10.8 kB
  • 15 Kernel SVM/107 Mapping to a higher dimension-en.srt 10.8 kB
  • 05 Multiple Linear Regression/037 Multiple Linear Regression Intuition - Step 3-tr.srt 10.8 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/011 Importing the Libraries-ja.srt 10.8 kB
  • 09 Random Forest Regression/074 Random Forest Regression Intuition-pt.srt 10.8 kB
  • 31 Artificial Neural Networks/235 ANN in R - Step 2-tr.srt 10.7 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/206 Natural Language Processing in R - Step 6-ja.srt 10.6 kB
  • 31 Artificial Neural Networks/233 ANN in Python - Step 10-en.srt 10.6 kB
  • 05 Multiple Linear Regression/037 Multiple Linear Regression Intuition - Step 3-en.srt 10.6 kB
  • 19 Evaluating Classification Models Performance/132 CAP Curve Analysis-ja.srt 10.6 kB
  • 12 Logistic Regression/086 Logistic Regression in Python - Step 1-ja.srt 10.6 kB
  • 09 Random Forest Regression/074 Random Forest Regression Intuition-it.srt 10.5 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/203 Natural Language Processing in R - Step 3-tr.srt 10.5 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/196 Natural Language Processing in Python - Step 7-tr.srt 10.4 kB
  • 31 Artificial Neural Networks/232 ANN in Python - Step 9-es.srt 10.4 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/203 Natural Language Processing in R - Step 3-en.srt 10.4 kB
  • 31 Artificial Neural Networks/235 ANN in R - Step 2-en.srt 10.4 kB
  • 09 Random Forest Regression/074 Random Forest Regression Intuition-es.srt 10.4 kB
  • 09 Random Forest Regression/074 Random Forest Regression Intuition-tr.srt 10.3 kB
  • 31 Artificial Neural Networks/232 ANN in Python - Step 9-it.srt 10.3 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/198 Natural Language Processing in Python - Step 9-ja.srt 10.2 kB
  • 16 Naive Bayes/115 Naive Bayes Intuition (Challenge Reveal)-ja.srt 10.2 kB
  • 04 Simple Linear Regression/027 Simple Linear Regression in Python - Step 3-es.srt 10.2 kB
  • 09 Random Forest Regression/074 Random Forest Regression Intuition-en.srt 10.1 kB
  • 31 Artificial Neural Networks/232 ANN in Python - Step 9-pt.srt 10.1 kB
  • 04 Simple Linear Regression/030 Simple Linear Regression in R - Step 2-ja.srt 10.1 kB
  • 04 Simple Linear Regression/027 Simple Linear Regression in Python - Step 3-it.srt 10.1 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/208 Natural Language Processing in R - Step 8-ja.srt 10.1 kB
  • 32 Convolutional Neural Networks/254 CNN in Python - Step 7-it.srt 10.1 kB
  • 04 Simple Linear Regression/027 Simple Linear Regression in Python - Step 3-pt.srt 10.1 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/196 Natural Language Processing in Python - Step 7-en.srt 10.0 kB
  • 32 Convolutional Neural Networks/254 CNN in Python - Step 7-es.srt 10.0 kB
  • 16 Naive Bayes/115 Naive Bayes Intuition (Challenge Reveal)-pt.srt 10.0 kB
  • 01 Welcome to the course/002 Why Machine Learning is the Future-pt.srt 10.0 kB
  • 01 Welcome to the course/002 Why Machine Learning is the Future-es.srt 9.9 kB
  • 06 Polynomial Regression/059 Polynomial Regression in Python - Step 4-ja.srt 9.9 kB
  • 32 Convolutional Neural Networks/254 CNN in Python - Step 7-pt.srt 9.9 kB
  • 22 Hierarchical Clustering/146 HC in Python - Step 2-es.srt 9.9 kB
  • 16 Naive Bayes/115 Naive Bayes Intuition (Challenge Reveal)-es.srt 9.9 kB
  • 32 Convolutional Neural Networks/241 Step 1(b) - ReLU Layer-pt.srt 9.9 kB
  • 25 Eclat/165 Eclat Intuition-ja.srt 9.9 kB
  • 01 Welcome to the course/002 Why Machine Learning is the Future-it.srt 9.9 kB
  • 22 Hierarchical Clustering/146 HC in Python - Step 2-it.srt 9.8 kB
  • 32 Convolutional Neural Networks/241 Step 1(b) - ReLU Layer-es.srt 9.8 kB
  • 31 Artificial Neural Networks/232 ANN in Python - Step 9-tr.srt 9.7 kB
  • 31 Artificial Neural Networks/232 ANN in Python - Step 9-en.srt 9.7 kB
  • 04 Simple Linear Regression/027 Simple Linear Regression in Python - Step 3-en.srt 9.7 kB
  • 04 Simple Linear Regression/027 Simple Linear Regression in Python - Step 3-tr.srt 9.7 kB
  • 22 Hierarchical Clustering/146 HC in Python - Step 2-pt.srt 9.7 kB
  • 32 Convolutional Neural Networks/241 Step 1(b) - ReLU Layer-it.srt 9.7 kB
  • 16 Naive Bayes/115 Naive Bayes Intuition (Challenge Reveal)-it.srt 9.7 kB
  • 32 Convolutional Neural Networks/241 Step 1(b) - ReLU Layer-tr.srt 9.6 kB
  • 32 Convolutional Neural Networks/254 CNN in Python - Step 7-tr.srt 9.6 kB
  • 01 Welcome to the course/007 Installing R and R Studio (Mac Linux Windows)-pt.srt 9.6 kB
  • 12 Logistic Regression/092 Logistic Regression in R - Step 1-es.srt 9.5 kB
  • 01 Welcome to the course/002 Why Machine Learning is the Future-tr.srt 9.5 kB
  • 01 Welcome to the course/002 Why Machine Learning is the Future-en.srt 9.5 kB
  • 19 Evaluating Classification Models Performance/132 CAP Curve Analysis-pt.srt 9.4 kB
  • 32 Convolutional Neural Networks/241 Step 1(b) - ReLU Layer-en.srt 9.4 kB
  • 01 Welcome to the course/007 Installing R and R Studio (Mac Linux Windows)-es.srt 9.4 kB
  • 19 Evaluating Classification Models Performance/132 CAP Curve Analysis-es.srt 9.4 kB
  • 22 Hierarchical Clustering/146 HC in Python - Step 2-en.srt 9.4 kB
  • 16 Naive Bayes/115 Naive Bayes Intuition (Challenge Reveal)-en.srt 9.4 kB
  • 32 Convolutional Neural Networks/252 CNN in Python - Step 5-ja.srt 9.4 kB
  • 32 Convolutional Neural Networks/254 CNN in Python - Step 7-en.srt 9.3 kB
  • 19 Evaluating Classification Models Performance/132 CAP Curve Analysis-it.srt 9.3 kB
  • 01 Welcome to the course/007 Installing R and R Studio (Mac Linux Windows)-it.srt 9.3 kB
  • 22 Hierarchical Clustering/146 HC in Python - Step 2-tr.srt 9.3 kB
  • 19 Evaluating Classification Models Performance/132 CAP Curve Analysis-tr.srt 9.3 kB
  • 22 Hierarchical Clustering/151 HC in R - Step 2-ja.srt 9.3 kB
  • 01 Welcome to the course/007 Installing R and R Studio (Mac Linux Windows)-tr.srt 9.2 kB
  • 12 Logistic Regression/092 Logistic Regression in R - Step 1-pt.srt 9.2 kB
  • 22 Hierarchical Clustering/147 HC in Python - Step 3-ja.srt 9.2 kB
  • 12 Logistic Regression/092 Logistic Regression in R - Step 1-it.srt 9.2 kB
  • 12 Logistic Regression/086 Logistic Regression in Python - Step 1-es.srt 9.2 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/198 Natural Language Processing in Python - Step 9-es.srt 9.2 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/206 Natural Language Processing in R - Step 6-es.srt 9.2 kB
  • 04 Simple Linear Regression/030 Simple Linear Regression in R - Step 2-es.srt 9.2 kB
  • 05 Multiple Linear Regression/043 Multiple Linear Regression in Python - Step 3-ja.srt 9.2 kB
  • 04 Simple Linear Regression/030 Simple Linear Regression in R - Step 2-pt.srt 9.1 kB
  • 16 Naive Bayes/115 Naive Bayes Intuition (Challenge Reveal)-tr.srt 9.1 kB
  • 19 Evaluating Classification Models Performance/132 CAP Curve Analysis-en.srt 9.1 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/198 Natural Language Processing in Python - Step 9-it.srt 9.1 kB
  • 04 Simple Linear Regression/023 Simple Linear Regression Intuition - Step 1-ja.srt 9.1 kB
  • 06 Polynomial Regression/059 Polynomial Regression in Python - Step 4-es.srt 9.1 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/198 Natural Language Processing in Python - Step 9-pt.srt 9.1 kB
  • 22 Hierarchical Clustering/145 HC in Python - Step 1-ja.srt 9.1 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/206 Natural Language Processing in R - Step 6-it.srt 9.1 kB
  • 12 Logistic Regression/086 Logistic Regression in Python - Step 1-pt.srt 9.1 kB
  • 12 Logistic Regression/086 Logistic Regression in Python - Step 1-it.srt 9.0 kB
  • 01 Welcome to the course/007 Installing R and R Studio (Mac Linux Windows)-en.srt 9.0 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/206 Natural Language Processing in R - Step 6-pt.srt 9.0 kB
  • 32 Convolutional Neural Networks/253 CNN in Python - Step 6-ja.srt 9.0 kB
  • 06 Polynomial Regression/059 Polynomial Regression in Python - Step 4-it.srt 9.0 kB
  • 13 K-Nearest Neighbors (K-NN)/098 K-Nearest Neighbor Intuition-ja.srt 9.0 kB
  • 04 Simple Linear Regression/030 Simple Linear Regression in R - Step 2-it.srt 8.9 kB
  • 12 Logistic Regression/092 Logistic Regression in R - Step 1-tr.srt 8.9 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/198 Natural Language Processing in Python - Step 9-tr.srt 8.9 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/011 Importing the Libraries-pt.srt 8.9 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/011 Importing the Libraries-es.srt 8.9 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/208 Natural Language Processing in R - Step 8-es.srt 8.9 kB
  • 06 Polynomial Regression/059 Polynomial Regression in Python - Step 4-pt.srt 8.9 kB
  • 12 Logistic Regression/086 Logistic Regression in Python - Step 1-tr.srt 8.9 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/011 Importing the Libraries-tr.srt 8.8 kB
  • 04 Simple Linear Regression/029 Simple Linear Regression in R - Step 1-ja.srt 8.8 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/206 Natural Language Processing in R - Step 6-tr.srt 8.8 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/208 Natural Language Processing in R - Step 8-it.srt 8.8 kB
  • 25 Eclat/165 Eclat Intuition-es.srt 8.8 kB
  • 25 Eclat/165 Eclat Intuition-pt.srt 8.8 kB
  • 04 Simple Linear Regression/030 Simple Linear Regression in R - Step 2-en.srt 8.8 kB
  • 12 Logistic Regression/092 Logistic Regression in R - Step 1-en.srt 8.8 kB
  • 04 Simple Linear Regression/030 Simple Linear Regression in R - Step 2-tr.srt 8.7 kB
  • 06 Polynomial Regression/054 Polynomial Regression Intuition-ja.srt 8.7 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/208 Natural Language Processing in R - Step 8-pt.srt 8.7 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/011 Importing the Libraries-it.srt 8.6 kB
  • 19 Evaluating Classification Models Performance/129 Confusion Matrix-ja.srt 8.6 kB
  • 06 Polynomial Regression/059 Polynomial Regression in Python - Step 4-tr.srt 8.6 kB
  • 12 Logistic Regression/086 Logistic Regression in Python - Step 1-en.srt 8.6 kB
  • 25 Eclat/165 Eclat Intuition-it.srt 8.6 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/206 Natural Language Processing in R - Step 6-en.srt 8.6 kB
  • 31 Artificial Neural Networks/223 Business Problem Description-ja.srt 8.5 kB
  • 06 Polynomial Regression/059 Polynomial Regression in Python - Step 4-en.srt 8.5 kB
  • 31 Artificial Neural Networks/221 Backpropagation-ja.srt 8.5 kB
  • 04 Simple Linear Regression/023 Simple Linear Regression Intuition - Step 1-es.srt 8.5 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/208 Natural Language Processing in R - Step 8-tr.srt 8.5 kB
  • 05 Multiple Linear Regression/043 Multiple Linear Regression in Python - Step 3-es.srt 8.5 kB
  • 04 Simple Linear Regression/023 Simple Linear Regression Intuition - Step 1-it.srt 8.5 kB
  • 32 Convolutional Neural Networks/252 CNN in Python - Step 5-es.srt 8.5 kB
  • 22 Hierarchical Clustering/151 HC in R - Step 2-es.srt 8.5 kB
  • 14 Support Vector Machine (SVM)/105 SVM.zip 8.5 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/198 Natural Language Processing in Python - Step 9-en.srt 8.4 kB
  • 10 Evaluating Regression Models Performance/078 R-Squared Intuition-ja.srt 8.4 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/188 Natural Language Processing Intuition-ja.srt 8.4 kB
  • 04 Simple Linear Regression/023 Simple Linear Regression Intuition - Step 1-pt.srt 8.4 kB
  • 32 Convolutional Neural Networks/252 CNN in Python - Step 5-it.srt 8.4 kB
  • 32 Convolutional Neural Networks/253 CNN in Python - Step 6-it.srt 8.4 kB
  • 18 Random Forest Classification/124 Random Forest Classification Intuition-ja.srt 8.4 kB
  • 32 Convolutional Neural Networks/253 CNN in Python - Step 6-es.srt 8.4 kB
  • 04 Simple Linear Regression/029 Simple Linear Regression in R - Step 1-es.srt 8.3 kB
  • 13 K-Nearest Neighbors (K-NN)/098 K-Nearest Neighbor Intuition-pt.srt 8.3 kB
  • 25 Eclat/165 Eclat Intuition-en.srt 8.3 kB
  • 05 Multiple Linear Regression/043 Multiple Linear Regression in Python - Step 3-pt.srt 8.3 kB
  • 22 Hierarchical Clustering/147 HC in Python - Step 3-es.srt 8.3 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/011 Importing the Libraries-en.srt 8.3 kB
  • 22 Hierarchical Clustering/151 HC in R - Step 2-pt.srt 8.3 kB
  • 22 Hierarchical Clustering/151 HC in R - Step 2-it.srt 8.2 kB
  • 12 Logistic Regression/094 Logistic Regression in R - Step 3-ja.srt 8.2 kB
  • 13 K-Nearest Neighbors (K-NN)/098 K-Nearest Neighbor Intuition-es.srt 8.2 kB
  • 04 Simple Linear Regression/023 Simple Linear Regression Intuition - Step 1-tr.srt 8.2 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/208 Natural Language Processing in R - Step 8-en.srt 8.2 kB
  • 04 Simple Linear Regression/023 Simple Linear Regression Intuition - Step 1-en.srt 8.2 kB
  • 25 Eclat/165 Eclat Intuition-tr.srt 8.2 kB
  • 05 Multiple Linear Regression/043 Multiple Linear Regression in Python - Step 3-it.srt 8.2 kB
  • 32 Convolutional Neural Networks/252 CNN in Python - Step 5-pt.srt 8.2 kB
  • 32 Convolutional Neural Networks/253 CNN in Python - Step 6-pt.srt 8.2 kB
  • 12 Logistic Regression/089 Logistic Regression in Python - Step 4-ja.srt 8.2 kB
  • 05 Multiple Linear Regression/043 Multiple Linear Regression in Python - Step 3-en.srt 8.1 kB
  • 05 Multiple Linear Regression/043 Multiple Linear Regression in Python - Step 3-tr.srt 8.1 kB
  • 04 Simple Linear Regression/029 Simple Linear Regression in R - Step 1-pt.srt 8.1 kB
  • 22 Hierarchical Clustering/145 HC in Python - Step 1-es.srt 8.1 kB
  • 06 Polynomial Regression/054 Polynomial Regression Intuition-es.srt 8.1 kB
  • 22 Hierarchical Clustering/147 HC in Python - Step 3-pt.srt 8.1 kB
  • 06 Polynomial Regression/054 Polynomial Regression Intuition-pt.srt 8.0 kB
  • 32 Convolutional Neural Networks/253 CNN in Python - Step 6-tr.srt 8.0 kB
  • 22 Hierarchical Clustering/151 HC in R - Step 2-en.srt 8.0 kB
  • 13 K-Nearest Neighbors (K-NN)/098 K-Nearest Neighbor Intuition-it.srt 8.0 kB
  • 05 Multiple Linear Regression/050 Multiple Linear Regression in R - Step 3-ja.srt 8.0 kB
  • 04 Simple Linear Regression/029 Simple Linear Regression in R - Step 1-it.srt 8.0 kB
  • 06 Polynomial Regression/054 Polynomial Regression Intuition-it.srt 8.0 kB
  • 32 Convolutional Neural Networks/252 CNN in Python - Step 5-tr.srt 8.0 kB
  • 22 Hierarchical Clustering/145 HC in Python - Step 1-pt.srt 8.0 kB
  • 12 Logistic Regression/097 R Classification Template-ja.srt 8.0 kB
  • 22 Hierarchical Clustering/147 HC in Python - Step 3-it.srt 7.9 kB
  • 13 K-Nearest Neighbors (K-NN)/098 K-Nearest Neighbor Intuition-en.srt 7.9 kB
  • 22 Hierarchical Clustering/149 HC in Python - Step 5-ja.srt 7.9 kB
  • 22 Hierarchical Clustering/151 HC in R - Step 2-tr.srt 7.9 kB
  • 31 Artificial Neural Networks/223 Business Problem Description-es.srt 7.9 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/188 Natural Language Processing Intuition-es.srt 7.9 kB
  • 13 K-Nearest Neighbors (K-NN)/098 K-Nearest Neighbor Intuition-tr.srt 7.9 kB
  • 22 Hierarchical Clustering/145 HC in Python - Step 1-it.srt 7.8 kB
  • 31 Artificial Neural Networks/223 Business Problem Description-pt.srt 7.8 kB
  • 32 Convolutional Neural Networks/253 CNN in Python - Step 6-en.srt 7.8 kB
  • 31 Artificial Neural Networks/223 Business Problem Description-it.srt 7.8 kB
  • 04 Simple Linear Regression/029 Simple Linear Regression in R - Step 1-tr.srt 7.8 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/188 Natural Language Processing Intuition-pt.srt 7.8 kB
  • 22 Hierarchical Clustering/148 HC in Python - Step 4-ja.srt 7.8 kB
  • 06 Polynomial Regression/054 Polynomial Regression Intuition-en.srt 7.7 kB
  • 06 Polynomial Regression/054 Polynomial Regression Intuition-tr.srt 7.7 kB
  • 12 Logistic Regression/094 Logistic Regression in R - Step 3-es.srt 7.7 kB
  • 31 Artificial Neural Networks/223 Business Problem Description-tr.srt 7.7 kB
  • 32 Convolutional Neural Networks/252 CNN in Python - Step 5-en.srt 7.7 kB
  • 22 Hierarchical Clustering/145 HC in Python - Step 1-tr.srt 7.6 kB
  • 31 Artificial Neural Networks/221 Backpropagation-es.srt 7.6 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/188 Natural Language Processing Intuition-tr.srt 7.6 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/188 Natural Language Processing Intuition-it.srt 7.6 kB
  • 31 Artificial Neural Networks/221 Backpropagation-pt.srt 7.6 kB
  • 22 Hierarchical Clustering/147 HC in Python - Step 3-en.srt 7.6 kB
  • 19 Evaluating Classification Models Performance/129 Confusion Matrix-pt.srt 7.6 kB
  • 31 Artificial Neural Networks/221 Backpropagation-it.srt 7.6 kB
  • 12 Logistic Regression/094 Logistic Regression in R - Step 3-pt.srt 7.6 kB
  • 19 Evaluating Classification Models Performance/129 Confusion Matrix-es.srt 7.6 kB
  • 04 Simple Linear Regression/029 Simple Linear Regression in R - Step 1-en.srt 7.6 kB
  • 18 Random Forest Classification/124 Random Forest Classification Intuition-pt.srt 7.6 kB
  • 12 Logistic Regression/094 Logistic Regression in R - Step 3-it.srt 7.6 kB
  • 22 Hierarchical Clustering/147 HC in Python - Step 3-tr.srt 7.5 kB
  • 22 Hierarchical Clustering/150 HC in R - Step 1-ja.srt 7.5 kB
  • 31 Artificial Neural Networks/223 Business Problem Description-en.srt 7.5 kB
  • 19 Evaluating Classification Models Performance/129 Confusion Matrix-it.srt 7.5 kB
  • 22 Hierarchical Clustering/145 HC in Python - Step 1-en.srt 7.5 kB
  • 12 Logistic Regression/091 Python Classification Template-ja.srt 7.4 kB
  • 10 Evaluating Regression Models Performance/078 R-Squared Intuition-it.srt 7.4 kB
  • 10 Evaluating Regression Models Performance/078 R-Squared Intuition-es.srt 7.4 kB
  • 19 Evaluating Classification Models Performance/129 Confusion Matrix-en.srt 7.4 kB
  • 12 Logistic Regression/094 Logistic Regression in R - Step 3-tr.srt 7.4 kB
  • 31 Artificial Neural Networks/221 Backpropagation-tr.srt 7.4 kB
  • 12 Logistic Regression/089 Logistic Regression in Python - Step 4-es.srt 7.4 kB
  • 10 Evaluating Regression Models Performance/078 R-Squared Intuition-pt.srt 7.4 kB
  • 18 Random Forest Classification/124 Random Forest Classification Intuition-es.srt 7.4 kB
  • 32 Convolutional Neural Networks/245 Summary-ja.srt 7.3 kB
  • 19 Evaluating Classification Models Performance/129 Confusion Matrix-tr.srt 7.3 kB
  • 12 Logistic Regression/094 Logistic Regression in R - Step 3-en.srt 7.3 kB
  • 31 Artificial Neural Networks/221 Backpropagation-en.srt 7.3 kB
  • 10 Evaluating Regression Models Performance/078 R-Squared Intuition-tr.srt 7.3 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/188 Natural Language Processing Intuition-en.srt 7.2 kB
  • 12 Logistic Regression/089 Logistic Regression in Python - Step 4-pt.srt 7.2 kB
  • 18 Random Forest Classification/124 Random Forest Classification Intuition-tr.srt 7.2 kB
  • 18 Random Forest Classification/124 Random Forest Classification Intuition-it.srt 7.2 kB
  • 12 Logistic Regression/089 Logistic Regression in Python - Step 4-it.srt 7.2 kB
  • 05 Multiple Linear Regression/050 Multiple Linear Regression in R - Step 3-es.srt 7.2 kB
  • 12 Logistic Regression/097 R Classification Template-tr.srt 7.1 kB
  • 12 Logistic Regression/089 Logistic Regression in Python - Step 4-tr.srt 7.1 kB
  • 10 Evaluating Regression Models Performance/078 R-Squared Intuition-en.srt 7.1 kB
  • 22 Hierarchical Clustering/149 HC in Python - Step 5-es.srt 7.1 kB
  • 05 Multiple Linear Regression/050 Multiple Linear Regression in R - Step 3-pt.srt 7.1 kB
  • 12 Logistic Regression/097 R Classification Template-es.srt 7.0 kB
  • 05 Multiple Linear Regression/050 Multiple Linear Regression in R - Step 3-tr.srt 7.0 kB
  • 12 Logistic Regression/089 Logistic Regression in Python - Step 4-en.srt 7.0 kB
  • 05 Multiple Linear Regression/050 Multiple Linear Regression in R - Step 3-it.srt 7.0 kB
  • 18 Random Forest Classification/124 Random Forest Classification Intuition-en.srt 7.0 kB
  • 12 Logistic Regression/097 R Classification Template-pt.srt 6.9 kB
  • 22 Hierarchical Clustering/149 HC in Python - Step 5-pt.srt 6.9 kB
  • 05 Multiple Linear Regression/050 Multiple Linear Regression in R - Step 3-en.srt 6.9 kB
  • 22 Hierarchical Clustering/148 HC in Python - Step 4-es.srt 6.9 kB
  • 22 Hierarchical Clustering/148 HC in Python - Step 4-pt.srt 6.9 kB
  • 12 Logistic Regression/097 R Classification Template-it.srt 6.9 kB
  • 22 Hierarchical Clustering/149 HC in Python - Step 5-it.srt 6.8 kB
  • 22 Hierarchical Clustering/149 HC in Python - Step 5-tr.srt 6.8 kB
  • 28 Thompson Sampling/184 Thompson Sampling in Python - Step 2-ja.srt 6.8 kB
  • 22 Hierarchical Clustering/148 HC in Python - Step 4-it.srt 6.8 kB
  • 01 Welcome to the course/001 Applications of Machine Learning-ja.srt 6.7 kB
  • 32 Convolutional Neural Networks/238 Plan of attack-ja.srt 6.7 kB
  • 22 Hierarchical Clustering/149 HC in Python - Step 5-en.srt 6.7 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/207 Natural Language Processing in R - Step 7-ja.srt 6.7 kB
  • 31 Artificial Neural Networks/230 ANN in Python - Step 7-ja.srt 6.7 kB
  • 22 Hierarchical Clustering/150 HC in R - Step 1-es.srt 6.7 kB
  • 22 Hierarchical Clustering/148 HC in Python - Step 4-tr.srt 6.7 kB
  • 32 Convolutional Neural Networks/245 Summary-es.srt 6.6 kB
  • 12 Logistic Regression/097 R Classification Template-en.srt 6.6 kB
  • 22 Hierarchical Clustering/150 HC in R - Step 1-pt.srt 6.6 kB
  • 32 Convolutional Neural Networks/245 Summary-pt.srt 6.6 kB
  • 12 Logistic Regression/091 Python Classification Template-es.srt 6.6 kB
  • 22 Hierarchical Clustering/150 HC in R - Step 1-it.srt 6.6 kB
  • 32 Convolutional Neural Networks/245 Summary-it.srt 6.5 kB
  • 22 Hierarchical Clustering/150 HC in R - Step 1-tr.srt 6.5 kB
  • 05 Multiple Linear Regression/034 Dataset Business Problem Description-ja.srt 6.5 kB
  • 12 Logistic Regression/091 Python Classification Template-it.srt 6.4 kB
  • 12 Logistic Regression/091 Python Classification Template-pt.srt 6.4 kB
  • 22 Hierarchical Clustering/148 HC in Python - Step 4-en.srt 6.4 kB
  • 12 Logistic Regression/091 Python Classification Template-tr.srt 6.4 kB
  • 32 Convolutional Neural Networks/245 Summary-tr.srt 6.3 kB
  • 31 Artificial Neural Networks/226 ANN in Python - Step 3-ja.srt 6.3 kB
  • 22 Hierarchical Clustering/150 HC in R - Step 1-en.srt 6.2 kB
  • 27 Upper Confidence Bound (UCB)/175 Upper Confidence Bound in Python - Step 4-ja.srt 6.2 kB
  • 15 Kernel SVM/109 Types of Kernel Functions-ja.srt 6.2 kB
  • 28 Thompson Sampling/186 Thompson Sampling in R - Step 2-ja.srt 6.2 kB
  • 32 Convolutional Neural Networks/245 Summary-en.srt 6.2 kB
  • 28 Thompson Sampling/184 Thompson Sampling in Python - Step 2-es.srt 6.1 kB
  • 04 Simple Linear Regression/031 Simple Linear Regression in R - Step 3-ja.srt 6.1 kB
  • 28 Thompson Sampling/184 Thompson Sampling in Python - Step 2-it.srt 6.1 kB
  • 28 Thompson Sampling/184 Thompson Sampling in Python - Step 2-pt.srt 6.0 kB
  • 12 Logistic Regression/091 Python Classification Template-en.srt 6.0 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/207 Natural Language Processing in R - Step 7-es.srt 6.0 kB
  • 01 Welcome to the course/001 Applications of Machine Learning-pt.srt 5.9 kB
  • 31 Artificial Neural Networks/230 ANN in Python - Step 7-pt.srt 5.9 kB
  • 01 Welcome to the course/001 Applications of Machine Learning-it.srt 5.8 kB
  • 12 Logistic Regression/087 Logistic Regression in Python - Step 2-ja.srt 5.8 kB
  • 28 Thompson Sampling/184 Thompson Sampling in Python - Step 2-tr.srt 5.8 kB
  • 31 Artificial Neural Networks/230 ANN in Python - Step 7-es.srt 5.8 kB
  • 04 Simple Linear Regression/021 How to get the dataset-ja.srt 5.8 kB
  • 05 Multiple Linear Regression/033 How to get the dataset-ja.srt 5.8 kB
  • 06 Polynomial Regression/055 How to get the dataset-ja.srt 5.8 kB
  • 07 Support Vector Regression (SVR)/066 How to get the dataset-ja.srt 5.8 kB
  • 08 Decision Tree Regression/071 How to get the dataset-ja.srt 5.8 kB
  • 09 Random Forest Regression/075 How to get the dataset-ja.srt 5.8 kB
  • 12 Logistic Regression/085 How to get the dataset-ja.srt 5.8 kB
  • 13 K-Nearest Neighbors (K-NN)/099 How to get the dataset-ja.srt 5.8 kB
  • 14 Support Vector Machine (SVM)/103 How to get the dataset-ja.srt 5.8 kB
  • 15 Kernel SVM/110 How to get the dataset-ja.srt 5.8 kB
  • 16 Naive Bayes/117 How to get the dataset-ja.srt 5.8 kB
  • 17 Decision Tree Classification/121 How to get the dataset-ja.srt 5.8 kB
  • 18 Random Forest Classification/125 How to get the dataset-ja.srt 5.8 kB
  • 21 K-Means Clustering/138 How to get the dataset-ja.srt 5.8 kB
  • 22 Hierarchical Clustering/144 How to get the dataset-ja.srt 5.8 kB
  • 24 Apriori/158 How to get the dataset-ja.srt 5.8 kB
  • 25 Eclat/166 How to get the dataset-ja.srt 5.8 kB
  • 27 Upper Confidence Bound (UCB)/171 How to get the dataset-ja.srt 5.8 kB
  • 28 Thompson Sampling/182 How to get the dataset-ja.srt 5.8 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/189 How to get the dataset-ja.srt 5.8 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/207 Natural Language Processing in R - Step 7-it.srt 5.8 kB
  • 31 Artificial Neural Networks/222 How to get the dataset-ja.srt 5.8 kB
  • 32 Convolutional Neural Networks/247 How to get the dataset-ja.srt 5.8 kB
  • 34 Principal Component Analysis (PCA)/261 How to get the dataset-ja.srt 5.8 kB
  • 35 Linear Discriminant Analysis (LDA)/269 How to get the dataset-ja.srt 5.8 kB
  • 36 Kernel PCA/272 How to get the dataset-ja.srt 5.8 kB
  • 38 Model Selection/276 How to get the dataset-ja.srt 5.8 kB
  • 39 XGBoost/282 How to get the dataset-ja.srt 5.8 kB
  • 31 Artificial Neural Networks/230 ANN in Python - Step 7-it.srt 5.8 kB
  • 05 Multiple Linear Regression/034 Dataset Business Problem Description-pt.srt 5.8 kB
  • 32 Convolutional Neural Networks/255 CNN in Python - Step 8-ja.srt 5.8 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/207 Natural Language Processing in R - Step 7-pt.srt 5.8 kB
  • 05 Multiple Linear Regression/034 Dataset Business Problem Description-es.srt 5.8 kB
  • 34 Principal Component Analysis (PCA)/260 Principal Component Analysis (PCA) Intuition-ja.srt 5.8 kB
  • 35 Linear Discriminant Analysis (LDA)/268 Linear Discriminant Analysis (LDA) Intuition-ja.srt 5.8 kB
  • 05 Multiple Linear Regression/034 Dataset Business Problem Description-tr.srt 5.7 kB
  • 40 Bonus Lectures/286 YOUR SPECIAL BONUS.html 5.7 kB
  • 28 Thompson Sampling/184 Thompson Sampling in Python - Step 2-en.srt 5.7 kB
  • 01 Welcome to the course/001 Applications of Machine Learning-es.srt 5.7 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/207 Natural Language Processing in R - Step 7-tr.srt 5.7 kB
  • 05 Multiple Linear Regression/034 Dataset Business Problem Description-it.srt 5.7 kB
  • 31 Artificial Neural Networks/230 ANN in Python - Step 7-tr.srt 5.7 kB
  • 32 Convolutional Neural Networks/249 CNN in Python - Step 2-ja.srt 5.7 kB
  • 04 Simple Linear Regression/031 Simple Linear Regression in R - Step 3-es.srt 5.7 kB
  • 27 Upper Confidence Bound (UCB)/175 Upper Confidence Bound in Python - Step 4-es.srt 5.6 kB
  • 31 Artificial Neural Networks/230 ANN in Python - Step 7-en.srt 5.6 kB
  • 27 Upper Confidence Bound (UCB)/175 Upper Confidence Bound in Python - Step 4-it.srt 5.6 kB
  • 01 Welcome to the course/001 Applications of Machine Learning-tr.srt 5.6 kB
  • 32 Convolutional Neural Networks/238 Plan of attack-tr.srt 5.6 kB
  • 05 Multiple Linear Regression/034 Dataset Business Problem Description-en.srt 5.6 kB
  • 32 Convolutional Neural Networks/238 Plan of attack-es.srt 5.6 kB
  • 04 Simple Linear Regression/031 Simple Linear Regression in R - Step 3-pt.srt 5.6 kB
  • 04 Simple Linear Regression/031 Simple Linear Regression in R - Step 3-it.srt 5.6 kB
  • 28 Thompson Sampling/186 Thompson Sampling in R - Step 2-es.srt 5.6 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/195 Natural Language Processing in Python - Step 6-ja.srt 5.5 kB
  • 28 Thompson Sampling/186 Thompson Sampling in R - Step 2-it.srt 5.5 kB
  • 27 Upper Confidence Bound (UCB)/175 Upper Confidence Bound in Python - Step 4-pt.srt 5.5 kB
  • 27 Upper Confidence Bound (UCB)/179 Upper Confidence Bound in R - Step 4-ja.srt 5.5 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/207 Natural Language Processing in R - Step 7-en.srt 5.5 kB
  • 28 Thompson Sampling/186 Thompson Sampling in R - Step 2-pt.srt 5.5 kB
  • 22 Hierarchical Clustering/152 HC in R - Step 3-ja.srt 5.5 kB
  • 32 Convolutional Neural Networks/238 Plan of attack-pt.srt 5.5 kB
  • 35 Linear Discriminant Analysis (LDA)/268 Linear Discriminant Analysis (LDA) Intuition-it.srt 5.5 kB
  • 35 Linear Discriminant Analysis (LDA)/268 Linear Discriminant Analysis (LDA) Intuition-pt.srt 5.5 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/204 Natural Language Processing in R - Step 4-ja.srt 5.4 kB
  • 35 Linear Discriminant Analysis (LDA)/268 Linear Discriminant Analysis (LDA) Intuition-es.srt 5.4 kB
  • 31 Artificial Neural Networks/226 ANN in Python - Step 3-pt.srt 5.4 kB
  • 04 Simple Linear Regression/031 Simple Linear Regression in R - Step 3-en.srt 5.4 kB
  • 01 Welcome to the course/001 Applications of Machine Learning-en.srt 5.4 kB
  • 31 Artificial Neural Networks/226 ANN in Python - Step 3-es.srt 5.4 kB
  • 32 Convolutional Neural Networks/238 Plan of attack-it.srt 5.4 kB
  • 34 Principal Component Analysis (PCA)/260 Principal Component Analysis (PCA) Intuition-es.srt 5.4 kB
  • 34 Principal Component Analysis (PCA)/260 Principal Component Analysis (PCA) Intuition-it.srt 5.4 kB
  • 32 Convolutional Neural Networks/238 Plan of attack-en.srt 5.4 kB
  • 31 Artificial Neural Networks/226 ANN in Python - Step 3-tr.srt 5.3 kB
  • 34 Principal Component Analysis (PCA)/260 Principal Component Analysis (PCA) Intuition-tr.srt 5.3 kB
  • 34 Principal Component Analysis (PCA)/260 Principal Component Analysis (PCA) Intuition-pt.srt 5.3 kB
  • 28 Thompson Sampling/186 Thompson Sampling in R - Step 2-tr.srt 5.3 kB
  • 31 Artificial Neural Networks/226 ANN in Python - Step 3-it.srt 5.3 kB
  • 27 Upper Confidence Bound (UCB)/175 Upper Confidence Bound in Python - Step 4-tr.srt 5.3 kB
  • 15 Kernel SVM/109 Types of Kernel Functions-es.srt 5.3 kB
  • 15 Kernel SVM/109 Types of Kernel Functions-pt.srt 5.3 kB
  • 15 Kernel SVM/106 Kernel SVM Intuition-ja.srt 5.2 kB
  • 35 Linear Discriminant Analysis (LDA)/268 Linear Discriminant Analysis (LDA) Intuition-en.srt 5.2 kB
  • 35 Linear Discriminant Analysis (LDA)/268 Linear Discriminant Analysis (LDA) Intuition-tr.srt 5.2 kB
  • 31 Artificial Neural Networks/229 ANN in Python - Step 6-ja.srt 5.2 kB
  • 28 Thompson Sampling/186 Thompson Sampling in R - Step 2-en.srt 5.2 kB
  • 04 Simple Linear Regression/021 How to get the dataset-es.srt 5.2 kB
  • 05 Multiple Linear Regression/033 How to get the dataset-es.srt 5.2 kB
  • 06 Polynomial Regression/055 How to get the dataset-es.srt 5.2 kB
  • 07 Support Vector Regression (SVR)/066 How to get the dataset-es.srt 5.2 kB
  • 08 Decision Tree Regression/071 How to get the dataset-es.srt 5.2 kB
  • 09 Random Forest Regression/075 How to get the dataset-es.srt 5.2 kB
  • 12 Logistic Regression/085 How to get the dataset-es.srt 5.2 kB
  • 13 K-Nearest Neighbors (K-NN)/099 How to get the dataset-es.srt 5.2 kB
  • 14 Support Vector Machine (SVM)/103 How to get the dataset-es.srt 5.2 kB
  • 15 Kernel SVM/110 How to get the dataset-es.srt 5.2 kB
  • 16 Naive Bayes/117 How to get the dataset-es.srt 5.2 kB
  • 17 Decision Tree Classification/121 How to get the dataset-es.srt 5.2 kB
  • 18 Random Forest Classification/125 How to get the dataset-es.srt 5.2 kB
  • 21 K-Means Clustering/138 How to get the dataset-es.srt 5.2 kB
  • 22 Hierarchical Clustering/144 How to get the dataset-es.srt 5.2 kB
  • 24 Apriori/158 How to get the dataset-es.srt 5.2 kB
  • 25 Eclat/166 How to get the dataset-es.srt 5.2 kB
  • 27 Upper Confidence Bound (UCB)/171 How to get the dataset-es.srt 5.2 kB
  • 28 Thompson Sampling/182 How to get the dataset-es.srt 5.2 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/189 How to get the dataset-es.srt 5.2 kB
  • 31 Artificial Neural Networks/222 How to get the dataset-es.srt 5.2 kB
  • 32 Convolutional Neural Networks/247 How to get the dataset-es.srt 5.2 kB
  • 34 Principal Component Analysis (PCA)/261 How to get the dataset-es.srt 5.2 kB
  • 35 Linear Discriminant Analysis (LDA)/269 How to get the dataset-es.srt 5.2 kB
  • 36 Kernel PCA/272 How to get the dataset-es.srt 5.2 kB
  • 38 Model Selection/276 How to get the dataset-es.srt 5.2 kB
  • 39 XGBoost/282 How to get the dataset-es.srt 5.2 kB
  • 31 Artificial Neural Networks/214 Plan of attack-ja.srt 5.2 kB
  • 15 Kernel SVM/109 Types of Kernel Functions-it.srt 5.2 kB
  • 04 Simple Linear Regression/031 Simple Linear Regression in R - Step 3-tr.srt 5.2 kB
  • 34 Principal Component Analysis (PCA)/260 Principal Component Analysis (PCA) Intuition-en.srt 5.2 kB
  • 15 Kernel SVM/109 Types of Kernel Functions-tr.srt 5.2 kB
  • 04 Simple Linear Regression/021 How to get the dataset-pt.srt 5.2 kB
  • 05 Multiple Linear Regression/033 How to get the dataset-pt.srt 5.2 kB
  • 07 Support Vector Regression (SVR)/066 How to get the dataset-pt.srt 5.2 kB
  • 08 Decision Tree Regression/071 How to get the dataset-pt.srt 5.2 kB
  • 09 Random Forest Regression/075 How to get the dataset-pt.srt 5.2 kB
  • 12 Logistic Regression/085 How to get the dataset-pt.srt 5.2 kB
  • 13 K-Nearest Neighbors (K-NN)/099 How to get the dataset-pt.srt 5.2 kB
  • 14 Support Vector Machine (SVM)/103 How to get the dataset-pt.srt 5.2 kB
  • 15 Kernel SVM/110 How to get the dataset-pt.srt 5.2 kB
  • 16 Naive Bayes/117 How to get the dataset-pt.srt 5.2 kB
  • 17 Decision Tree Classification/121 How to get the dataset-pt.srt 5.2 kB
  • 18 Random Forest Classification/125 How to get the dataset-pt.srt 5.2 kB
  • 21 K-Means Clustering/138 How to get the dataset-pt.srt 5.2 kB
  • 22 Hierarchical Clustering/144 How to get the dataset-pt.srt 5.2 kB
  • 24 Apriori/158 How to get the dataset-pt.srt 5.2 kB
  • 25 Eclat/166 How to get the dataset-pt.srt 5.2 kB
  • 27 Upper Confidence Bound (UCB)/171 How to get the dataset-pt.srt 5.2 kB
  • 28 Thompson Sampling/182 How to get the dataset-pt.srt 5.2 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/189 How to get the dataset-pt.srt 5.2 kB
  • 31 Artificial Neural Networks/222 How to get the dataset-pt.srt 5.2 kB
  • 32 Convolutional Neural Networks/247 How to get the dataset-pt.srt 5.2 kB
  • 34 Principal Component Analysis (PCA)/261 How to get the dataset-pt.srt 5.2 kB
  • 35 Linear Discriminant Analysis (LDA)/269 How to get the dataset-pt.srt 5.2 kB
  • 36 Kernel PCA/272 How to get the dataset-pt.srt 5.2 kB
  • 38 Model Selection/276 How to get the dataset-pt.srt 5.2 kB
  • 39 XGBoost/282 How to get the dataset-pt.srt 5.2 kB
  • 06 Polynomial Regression/055 How to get the dataset-pt.srt 5.1 kB
  • 12 Logistic Regression/087 Logistic Regression in Python - Step 2-es.srt 5.1 kB
  • 27 Upper Confidence Bound (UCB)/175 Upper Confidence Bound in Python - Step 4-en.srt 5.1 kB
  • 31 Artificial Neural Networks/226 ANN in Python - Step 3-en.srt 5.1 kB
  • 04 Simple Linear Regression/021 How to get the dataset-it.srt 5.1 kB
  • 05 Multiple Linear Regression/033 How to get the dataset-it.srt 5.1 kB
  • 06 Polynomial Regression/055 How to get the dataset-it.srt 5.1 kB
  • 07 Support Vector Regression (SVR)/066 How to get the dataset-it.srt 5.1 kB
  • 08 Decision Tree Regression/071 How to get the dataset-it.srt 5.1 kB
  • 09 Random Forest Regression/075 How to get the dataset-it.srt 5.1 kB
  • 12 Logistic Regression/085 How to get the dataset-it.srt 5.1 kB
  • 13 K-Nearest Neighbors (K-NN)/099 How to get the dataset-it.srt 5.1 kB
  • 14 Support Vector Machine (SVM)/103 How to get the dataset-it.srt 5.1 kB
  • 15 Kernel SVM/110 How to get the dataset-it.srt 5.1 kB
  • 16 Naive Bayes/117 How to get the dataset-it.srt 5.1 kB
  • 17 Decision Tree Classification/121 How to get the dataset-it.srt 5.1 kB
  • 18 Random Forest Classification/125 How to get the dataset-it.srt 5.1 kB
  • 21 K-Means Clustering/138 How to get the dataset-it.srt 5.1 kB
  • 22 Hierarchical Clustering/144 How to get the dataset-it.srt 5.1 kB
  • 24 Apriori/158 How to get the dataset-it.srt 5.1 kB
  • 25 Eclat/166 How to get the dataset-it.srt 5.1 kB
  • 27 Upper Confidence Bound (UCB)/171 How to get the dataset-it.srt 5.1 kB
  • 28 Thompson Sampling/182 How to get the dataset-it.srt 5.1 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/189 How to get the dataset-it.srt 5.1 kB
  • 31 Artificial Neural Networks/222 How to get the dataset-it.srt 5.1 kB
  • 32 Convolutional Neural Networks/247 How to get the dataset-it.srt 5.1 kB
  • 34 Principal Component Analysis (PCA)/261 How to get the dataset-it.srt 5.1 kB
  • 35 Linear Discriminant Analysis (LDA)/269 How to get the dataset-it.srt 5.1 kB
  • 36 Kernel PCA/272 How to get the dataset-it.srt 5.1 kB
  • 38 Model Selection/276 How to get the dataset-it.srt 5.1 kB
  • 39 XGBoost/282 How to get the dataset-it.srt 5.1 kB
  • 15 Kernel SVM/109 Types of Kernel Functions-en.srt 5.1 kB
  • 12 Logistic Regression/093 Logistic Regression in R - Step 2-ja.srt 5.0 kB
  • 12 Logistic Regression/087 Logistic Regression in Python - Step 2-pt.srt 5.0 kB
  • 12 Logistic Regression/087 Logistic Regression in Python - Step 2-it.srt 5.0 kB
  • 27 Upper Confidence Bound (UCB)/179 Upper Confidence Bound in R - Step 4-es.srt 5.0 kB
  • 32 Convolutional Neural Networks/255 CNN in Python - Step 8-pt.srt 5.0 kB
  • 27 Upper Confidence Bound (UCB)/179 Upper Confidence Bound in R - Step 4-it.srt 5.0 kB
  • 32 Convolutional Neural Networks/255 CNN in Python - Step 8-it.srt 5.0 kB
  • 32 Convolutional Neural Networks/255 CNN in Python - Step 8-es.srt 5.0 kB
  • 04 Simple Linear Regression/021 How to get the dataset-tr.srt 5.0 kB
  • 05 Multiple Linear Regression/033 How to get the dataset-tr.srt 5.0 kB
  • 06 Polynomial Regression/055 How to get the dataset-tr.srt 5.0 kB
  • 07 Support Vector Regression (SVR)/066 How to get the dataset-tr.srt 5.0 kB
  • 08 Decision Tree Regression/071 How to get the dataset-tr.srt 5.0 kB
  • 09 Random Forest Regression/075 How to get the dataset-tr.srt 5.0 kB
  • 12 Logistic Regression/085 How to get the dataset-tr.srt 5.0 kB
  • 13 K-Nearest Neighbors (K-NN)/099 How to get the dataset-tr.srt 5.0 kB
  • 14 Support Vector Machine (SVM)/103 How to get the dataset-tr.srt 5.0 kB
  • 15 Kernel SVM/110 How to get the dataset-tr.srt 5.0 kB
  • 16 Naive Bayes/117 How to get the dataset-tr.srt 5.0 kB
  • 17 Decision Tree Classification/121 How to get the dataset-tr.srt 5.0 kB
  • 18 Random Forest Classification/125 How to get the dataset-tr.srt 5.0 kB
  • 21 K-Means Clustering/138 How to get the dataset-tr.srt 5.0 kB
  • 22 Hierarchical Clustering/144 How to get the dataset-tr.srt 5.0 kB
  • 24 Apriori/158 How to get the dataset-tr.srt 5.0 kB
  • 25 Eclat/166 How to get the dataset-tr.srt 5.0 kB
  • 27 Upper Confidence Bound (UCB)/171 How to get the dataset-tr.srt 5.0 kB
  • 28 Thompson Sampling/182 How to get the dataset-tr.srt 5.0 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/189 How to get the dataset-tr.srt 5.0 kB
  • 31 Artificial Neural Networks/222 How to get the dataset-tr.srt 5.0 kB
  • 32 Convolutional Neural Networks/247 How to get the dataset-tr.srt 5.0 kB
  • 34 Principal Component Analysis (PCA)/261 How to get the dataset-tr.srt 5.0 kB
  • 35 Linear Discriminant Analysis (LDA)/269 How to get the dataset-tr.srt 5.0 kB
  • 36 Kernel PCA/272 How to get the dataset-tr.srt 5.0 kB
  • 38 Model Selection/276 How to get the dataset-tr.srt 5.0 kB
  • 39 XGBoost/282 How to get the dataset-tr.srt 5.0 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/204 Natural Language Processing in R - Step 4-es.srt 4.9 kB
  • 22 Hierarchical Clustering/152 HC in R - Step 3-it.srt 4.9 kB
  • 27 Upper Confidence Bound (UCB)/179 Upper Confidence Bound in R - Step 4-pt.srt 4.9 kB
  • 22 Hierarchical Clustering/152 HC in R - Step 3-es.srt 4.9 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/195 Natural Language Processing in Python - Step 6-es.srt 4.9 kB
  • 22 Hierarchical Clustering/152 HC in R - Step 3-pt.srt 4.9 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/204 Natural Language Processing in R - Step 4-it.srt 4.9 kB
  • 12 Logistic Regression/087 Logistic Regression in Python - Step 2-tr.srt 4.9 kB
  • 32 Convolutional Neural Networks/249 CNN in Python - Step 2-it.srt 4.9 kB
  • 15 Kernel SVM/106 Kernel SVM Intuition-es.srt 4.9 kB
  • 04 Simple Linear Regression/021 How to get the dataset-en.srt 4.9 kB
  • 05 Multiple Linear Regression/033 How to get the dataset-en.srt 4.9 kB
  • 06 Polynomial Regression/055 How to get the dataset-en.srt 4.9 kB
  • 07 Support Vector Regression (SVR)/066 How to get the dataset-en.srt 4.9 kB
  • 08 Decision Tree Regression/071 How to get the dataset-en.srt 4.9 kB
  • 09 Random Forest Regression/075 How to get the dataset-en.srt 4.9 kB
  • 12 Logistic Regression/085 How to get the dataset-en.srt 4.9 kB
  • 13 K-Nearest Neighbors (K-NN)/099 How to get the dataset-en.srt 4.9 kB
  • 14 Support Vector Machine (SVM)/103 How to get the dataset-en.srt 4.9 kB
  • 15 Kernel SVM/110 How to get the dataset-en.srt 4.9 kB
  • 16 Naive Bayes/117 How to get the dataset-en.srt 4.9 kB
  • 17 Decision Tree Classification/121 How to get the dataset-en.srt 4.9 kB
  • 18 Random Forest Classification/125 How to get the dataset-en.srt 4.9 kB
  • 21 K-Means Clustering/138 How to get the dataset-en.srt 4.9 kB
  • 22 Hierarchical Clustering/144 How to get the dataset-en.srt 4.9 kB
  • 24 Apriori/158 How to get the dataset-en.srt 4.9 kB
  • 25 Eclat/166 How to get the dataset-en.srt 4.9 kB
  • 27 Upper Confidence Bound (UCB)/171 How to get the dataset-en.srt 4.9 kB
  • 28 Thompson Sampling/182 How to get the dataset-en.srt 4.9 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/189 How to get the dataset-en.srt 4.9 kB
  • 31 Artificial Neural Networks/222 How to get the dataset-en.srt 4.9 kB
  • 32 Convolutional Neural Networks/247 How to get the dataset-en.srt 4.9 kB
  • 34 Principal Component Analysis (PCA)/261 How to get the dataset-en.srt 4.9 kB
  • 35 Linear Discriminant Analysis (LDA)/269 How to get the dataset-en.srt 4.9 kB
  • 36 Kernel PCA/272 How to get the dataset-en.srt 4.9 kB
  • 38 Model Selection/276 How to get the dataset-en.srt 4.9 kB
  • 39 XGBoost/282 How to get the dataset-en.srt 4.9 kB
  • 04 Simple Linear Regression/024 Simple Linear Regression Intuition - Step 2-ja.srt 4.9 kB
  • 12 Logistic Regression/087 Logistic Regression in Python - Step 2-en.srt 4.8 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/204 Natural Language Processing in R - Step 4-pt.srt 4.8 kB
  • 15 Kernel SVM/106 Kernel SVM Intuition-it.srt 4.8 kB
  • 05 Multiple Linear Regression/042 Multiple Linear Regression in Python - Step 2-ja.srt 4.8 kB
  • 15 Kernel SVM/106 Kernel SVM Intuition-pt.srt 4.8 kB
  • 32 Convolutional Neural Networks/249 CNN in Python - Step 2-es.srt 4.8 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/195 Natural Language Processing in Python - Step 6-pt.srt 4.8 kB
  • 12 Logistic Regression/088 Logistic Regression in Python - Step 3-ja.srt 4.8 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/204 Natural Language Processing in R - Step 4-tr.srt 4.8 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/195 Natural Language Processing in Python - Step 6-it.srt 4.8 kB
  • 32 Convolutional Neural Networks/249 CNN in Python - Step 2-pt.srt 4.8 kB
  • 27 Upper Confidence Bound (UCB)/179 Upper Confidence Bound in R - Step 4-tr.srt 4.7 kB
  • 22 Hierarchical Clustering/152 HC in R - Step 3-tr.srt 4.7 kB
  • 31 Artificial Neural Networks/227 ANN in Python - Step 4-ja.srt 4.7 kB
  • 32 Convolutional Neural Networks/255 CNN in Python - Step 8-tr.srt 4.7 kB
  • 31 Artificial Neural Networks/229 ANN in Python - Step 6-it.srt 4.7 kB
  • 32 Convolutional Neural Networks/255 CNN in Python - Step 8-en.srt 4.7 kB
  • 22 Hierarchical Clustering/152 HC in R - Step 3-en.srt 4.7 kB
  • 22 Hierarchical Clustering/154 HC in R - Step 5-ja.srt 4.7 kB
  • 19 Evaluating Classification Models Performance/133 Conclusion of Part 3 - Classification.html 4.6 kB
  • 22 Hierarchical Clustering/153 HC in R - Step 4-ja.srt 4.6 kB
  • 31 Artificial Neural Networks/229 ANN in Python - Step 6-es.srt 4.6 kB
  • 12 Logistic Regression/093 Logistic Regression in R - Step 2-es.srt 4.6 kB
  • 31 Artificial Neural Networks/229 ANN in Python - Step 6-pt.srt 4.6 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/204 Natural Language Processing in R - Step 4-en.srt 4.6 kB
  • 12 Logistic Regression/093 Logistic Regression in R - Step 2-pt.srt 4.6 kB
  • 32 Convolutional Neural Networks/249 CNN in Python - Step 2-en.srt 4.6 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/195 Natural Language Processing in Python - Step 6-tr.srt 4.6 kB
  • 32 Convolutional Neural Networks/249 CNN in Python - Step 2-tr.srt 4.6 kB
  • 15 Kernel SVM/106 Kernel SVM Intuition-tr.srt 4.5 kB
  • 27 Upper Confidence Bound (UCB)/179 Upper Confidence Bound in R - Step 4-en.srt 4.5 kB
  • 15 Kernel SVM/106 Kernel SVM Intuition-en.srt 4.5 kB
  • 12 Logistic Regression/093 Logistic Regression in R - Step 2-it.srt 4.5 kB
  • 04 Simple Linear Regression/022 Dataset Business Problem Description-ja.srt 4.5 kB
  • 12 Logistic Regression/095 Logistic Regression in R - Step 4-ja.srt 4.5 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/195 Natural Language Processing in Python - Step 6-en.srt 4.5 kB
  • 31 Artificial Neural Networks/229 ANN in Python - Step 6-tr.srt 4.5 kB
  • 04 Simple Linear Regression/024 Simple Linear Regression Intuition - Step 2-pt.srt 4.4 kB
  • 04 Simple Linear Regression/024 Simple Linear Regression Intuition - Step 2-es.srt 4.4 kB
  • 31 Artificial Neural Networks/229 ANN in Python - Step 6-en.srt 4.4 kB
  • 04 Simple Linear Regression/024 Simple Linear Regression Intuition - Step 2-it.srt 4.4 kB
  • 12 Logistic Regression/093 Logistic Regression in R - Step 2-tr.srt 4.4 kB
  • 22 Hierarchical Clustering/154 HC in R - Step 5-es.srt 4.3 kB
  • 12 Logistic Regression/093 Logistic Regression in R - Step 2-en.srt 4.3 kB
  • 04 Simple Linear Regression/024 Simple Linear Regression Intuition - Step 2-en.srt 4.3 kB
  • 12 Logistic Regression/088 Logistic Regression in Python - Step 3-es.srt 4.3 kB
  • 01 Welcome to the course/003 Important notes tips tricks for this course.html 4.3 kB
  • 05 Multiple Linear Regression/042 Multiple Linear Regression in Python - Step 2-it.srt 4.2 kB
  • 05 Multiple Linear Regression/042 Multiple Linear Regression in Python - Step 2-es.srt 4.2 kB
  • 04 Simple Linear Regression/024 Simple Linear Regression Intuition - Step 2-tr.srt 4.2 kB
  • 31 Artificial Neural Networks/214 Plan of attack-es.srt 4.2 kB
  • 22 Hierarchical Clustering/153 HC in R - Step 4-es.srt 4.2 kB
  • 22 Hierarchical Clustering/154 HC in R - Step 5-it.srt 4.2 kB
  • 05 Multiple Linear Regression/042 Multiple Linear Regression in Python - Step 2-pt.srt 4.2 kB
  • 04 Simple Linear Regression/022 Dataset Business Problem Description-pt.srt 4.2 kB
  • 22 Hierarchical Clustering/154 HC in R - Step 5-pt.srt 4.2 kB
  • 10 Evaluating Regression Models Performance/082 Conclusion of Part 2 - Regression.html 4.2 kB
  • 12 Logistic Regression/088 Logistic Regression in Python - Step 3-pt.srt 4.2 kB
  • 31 Artificial Neural Networks/214 Plan of attack-tr.srt 4.1 kB
  • 31 Artificial Neural Networks/214 Plan of attack-pt.srt 4.1 kB
  • 22 Hierarchical Clustering/153 HC in R - Step 4-pt.srt 4.1 kB
  • 04 Simple Linear Regression/022 Dataset Business Problem Description-es.srt 4.1 kB
  • 12 Logistic Regression/088 Logistic Regression in Python - Step 3-it.srt 4.1 kB
  • 22 Hierarchical Clustering/153 HC in R - Step 4-it.srt 4.1 kB
  • 31 Artificial Neural Networks/214 Plan of attack-en.srt 4.1 kB
  • 31 Artificial Neural Networks/214 Plan of attack-it.srt 4.1 kB
  • 12 Logistic Regression/095 Logistic Regression in R - Step 4-es.srt 4.1 kB
  • 22 Hierarchical Clustering/154 HC in R - Step 5-tr.srt 4.1 kB
  • 12 Logistic Regression/088 Logistic Regression in Python - Step 3-tr.srt 4.0 kB
  • 04 Simple Linear Regression/022 Dataset Business Problem Description-en.srt 4.0 kB
  • 12 Logistic Regression/088 Logistic Regression in Python - Step 3-en.srt 4.0 kB
  • 12 Logistic Regression/095 Logistic Regression in R - Step 4-pt.srt 4.0 kB
  • 05 Multiple Linear Regression/042 Multiple Linear Regression in Python - Step 2-en.srt 4.0 kB
  • 04 Simple Linear Regression/022 Dataset Business Problem Description-it.srt 4.0 kB
  • 22 Hierarchical Clustering/153 HC in R - Step 4-tr.srt 4.0 kB
  • 22 Hierarchical Clustering/154 HC in R - Step 5-en.srt 4.0 kB
  • 31 Artificial Neural Networks/227 ANN in Python - Step 4-it.srt 4.0 kB
  • 31 Artificial Neural Networks/227 ANN in Python - Step 4-pt.srt 4.0 kB
  • 31 Artificial Neural Networks/227 ANN in Python - Step 4-es.srt 4.0 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/205 Natural Language Processing in R - Step 5-ja.srt 4.0 kB
  • 04 Simple Linear Regression/022 Dataset Business Problem Description-tr.srt 4.0 kB
  • 12 Logistic Regression/095 Logistic Regression in R - Step 4-it.srt 3.9 kB
  • 12 Logistic Regression/095 Logistic Regression in R - Step 4-en.srt 3.9 kB
  • 31 Artificial Neural Networks/227 ANN in Python - Step 4-tr.srt 3.9 kB
  • 05 Multiple Linear Regression/042 Multiple Linear Regression in Python - Step 2-tr.srt 3.9 kB
  • 12 Logistic Regression/095 Logistic Regression in R - Step 4-tr.srt 3.8 kB
  • 05 Multiple Linear Regression/038 Multiple Linear Regression Intuition - Step 4-ja.srt 3.8 kB
  • 31 Artificial Neural Networks/227 ANN in Python - Step 4-en.srt 3.8 kB
  • 22 Hierarchical Clustering/153 HC in R - Step 4-en.srt 3.8 kB
  • 05 Multiple Linear Regression/038 Multiple Linear Regression Intuition - Step 4-pt.srt 3.6 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/205 Natural Language Processing in R - Step 5-es.srt 3.6 kB
  • 05 Multiple Linear Regression/038 Multiple Linear Regression Intuition - Step 4-es.srt 3.5 kB
  • 19 Evaluating Classification Models Performance/130 Accuracy Paradox-ja.srt 3.5 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/205 Natural Language Processing in R - Step 5-tr.srt 3.5 kB
  • 05 Multiple Linear Regression/038 Multiple Linear Regression Intuition - Step 4-it.srt 3.5 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/205 Natural Language Processing in R - Step 5-pt.srt 3.5 kB
  • 05 Multiple Linear Regression/038 Multiple Linear Regression Intuition - Step 4-en.srt 3.5 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/205 Natural Language Processing in R - Step 5-it.srt 3.4 kB
  • 05 Multiple Linear Regression/038 Multiple Linear Regression Intuition - Step 4-tr.srt 3.4 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/205 Natural Language Processing in R - Step 5-en.srt 3.3 kB
  • 19 Evaluating Classification Models Performance/130 Accuracy Paradox-es.srt 3.3 kB
  • 19 Evaluating Classification Models Performance/130 Accuracy Paradox-it.srt 3.3 kB
  • 32 Convolutional Neural Networks/258 CNN in R.html 3.3 kB
  • 19 Evaluating Classification Models Performance/130 Accuracy Paradox-pt.srt 3.3 kB
  • 19 Evaluating Classification Models Performance/130 Accuracy Paradox-en.srt 3.2 kB
  • 19 Evaluating Classification Models Performance/130 Accuracy Paradox-tr.srt 3.2 kB
  • 05 Multiple Linear Regression/047 Multiple Linear Regression in Python - Automatic Backward Elimination.html 3.1 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/192 Natural Language Processing in Python - Step 3-ja.srt 3.0 kB
  • 32 Convolutional Neural Networks/243 Step 3 - Flattening-ja.srt 3.0 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/009 Welcome to Part 1 - Data Preprocessing-ja.srt 3.0 kB
  • 32 Convolutional Neural Networks/243 Step 3 - Flattening-es.srt 2.8 kB
  • 32 Convolutional Neural Networks/243 Step 3 - Flattening-tr.srt 2.8 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/192 Natural Language Processing in Python - Step 3-es.srt 2.7 kB
  • 32 Convolutional Neural Networks/243 Step 3 - Flattening-pt.srt 2.7 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/192 Natural Language Processing in Python - Step 3-it.srt 2.7 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/009 Welcome to Part 1 - Data Preprocessing-pt.srt 2.7 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/192 Natural Language Processing in Python - Step 3-pt.srt 2.7 kB
  • 32 Convolutional Neural Networks/243 Step 3 - Flattening-it.srt 2.7 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/009 Welcome to Part 1 - Data Preprocessing-es.srt 2.7 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/009 Welcome to Part 1 - Data Preprocessing-it.srt 2.6 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/192 Natural Language Processing in Python - Step 3-tr.srt 2.6 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/187 Welcome to Part 7 - Natural Language Processing.html 2.6 kB
  • 32 Convolutional Neural Networks/243 Step 3 - Flattening-en.srt 2.6 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/009 Welcome to Part 1 - Data Preprocessing-tr.srt 2.6 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/192 Natural Language Processing in Python - Step 3-en.srt 2.5 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/009 Welcome to Part 1 - Data Preprocessing-en.srt 2.5 kB
  • 01 Welcome to the course/004 This PDF resource will help you a lot.html 2.4 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/013 For Python learners summary of Object-oriented programming classes objects.html 2.4 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/211 Homework Challenge.html 2.3 kB
  • 29 -------------------- Part 7 Natural Language Processing --------------------/200 Homework Challenge.html 2.3 kB
  • 01 Welcome to the course/006 Update Recommended Anaconda Version.html 2.2 kB
  • 33 -------------------- Part 9 Dimensionality Reduction --------------------/259 Welcome to Part 9 - Dimensionality Reduction.html 2.2 kB
  • 32 Convolutional Neural Networks/250 CNN in Python - Step 3-ja.srt 2.0 kB
  • 01 Welcome to the course/008 BONUS Meet your instructors.html 1.9 kB
  • 37 -------------------- Part 10 Model Selection Boosting --------------------/275 Welcome to Part 10 - Model Selection Boosting.html 1.8 kB
  • 32 Convolutional Neural Networks/250 CNN in Python - Step 3-tr.srt 1.8 kB
  • 32 Convolutional Neural Networks/250 CNN in Python - Step 3-es.srt 1.8 kB
  • 32 Convolutional Neural Networks/250 CNN in Python - Step 3-it.srt 1.8 kB
  • 32 Convolutional Neural Networks/250 CNN in Python - Step 3-pt.srt 1.7 kB
  • 30 -------------------- Part 8 Deep Learning --------------------/212 Welcome to Part 8 - Deep Learning.html 1.7 kB
  • 03 -------------------- Part 2 Regression --------------------/020 Welcome to Part 2 - Regression.html 1.7 kB
  • 11 -------------------- Part 3 Classification --------------------/083 Welcome to Part 3 - Classification.html 1.7 kB
  • 32 Convolutional Neural Networks/250 CNN in Python - Step 3-en.srt 1.7 kB
  • 26 -------------------- Part 6 Reinforcement Learning --------------------/168 Welcome to Part 6 - Reinforcement Learning.html 1.7 kB
  • 02 -------------------- Part 1 Data Preprocessing --------------------/016 WARNING - Update.html 1.6 kB
  • 05 Multiple Linear Regression/053 Multiple Linear Regression in R - Automatic Backward Elimination.html 1.6 kB
  • 05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 1-ja.srt 1.6 kB
  • 05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 1-es.srt 1.6 kB
  • 05 Multiple Linear Regression/036 Multiple Linear Regression Intuition - Step 2-ja.srt 1.6 kB
  • 20 -------------------- Part 4 Clustering --------------------/134 Welcome to Part 4 - Clustering.html 1.6 kB
  • 05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 1-tr.srt 1.6 kB
  • 05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 1-pt.srt 1.6 kB
  • 05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 1-en.srt 1.6 kB
  • 05 Multiple Linear Regression/039 Prerequisites What is the P-Value.html 1.5 kB
  • 05 Multiple Linear Regression/035 Multiple Linear Regression Intuition - Step 1-it.srt 1.5 kB
  • 05 Multiple Linear Regression/036 Multiple Linear Regression Intuition - Step 2-pt.srt 1.5 kB
  • 05 Multiple Linear Regression/036 Multiple Linear Regression Intuition - Step 2-tr.srt 1.5 kB
  • 05 Multiple Linear Regression/036 Multiple Linear Regression Intuition - Step 2-es.srt 1.5 kB
  • 05 Multiple Linear Regression/036 Multiple Linear Regression Intuition - Step 2-it.srt 1.4 kB
  • 05 Multiple Linear Regression/036 Multiple Linear Regression Intuition - Step 2-en.srt 1.4 kB
  • 22 Hierarchical Clustering/155 Conclusion of Part 4 - Clustering.html 1.4 kB
  • 23 -------------------- Part 5 Association Rule Learning --------------------/156 Welcome to Part 5 - Association Rule Learning.html 1.3 kB
  • udemycoursedownloader.com.url 132 Bytes
  • Udemy Course downloader.txt 94 Bytes

随机展示

相关说明

本站不存储任何资源内容，只收集BT种子元数据(例如文件名和文件大小)和磁力链接(BT种子标识符)，并提供查询服务，是一个完全合法的搜索引擎系统。 网站不提供种子下载服务，用户可以通过第三方链接或磁力链接获取到相关的种子资源。本站也不对BT种子真实性及合法性负责，请用户注意甄别！