[FreeTutorials.Eu] Udemy - Machine Learning A-Z Become Kaggle Master

磁力链接/BT种子名称

[FreeTutorials.Eu] Udemy - Machine Learning A-Z Become Kaggle Master

磁力链接/BT种子简介

种子哈希：4262230db3b95cedb1839b5e6dd665d05d43fe5d
文件大小： 13.97G
已经下载：1000次
下载速度：极快
收录时间：2021-03-17
最近下载：2025-09-14

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:4262230DB3B95CEDB1839B5E6DD665D05D43FE5D
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 抖音Max 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

  • 17. Logistic Regression/4. Case Study.mp4 207.8 MB
  • 7. Data Visualisation/2. Seaborn.mp4 193.7 MB
  • 15. Model Selection Part1/4. Gridsearch Case study Part2.mp4 187.6 MB
  • 7. Data Visualisation/1. Matplotlib.mp4 181.2 MB
  • 2. Numpy/3. Numpy Operations Part2.mp4 178.2 MB
  • 18. Support Vector Machine (SVM)/14. Case Study 4.mp4 172.4 MB
  • 4. Some Fun With Maths/1. Linear Algebra Vectors.mp4 170.3 MB
  • 23. Dimension Reduction/1. Introduction.mp4 164.3 MB
  • 20. Ensembling/16. Case Study Part1.mp4 148.4 MB
  • 9. Simple Linear Regression/7. LR Case Study Part1.mp4 144.2 MB
  • 26. Project Kaggle/2. Playing With The Data.mp4 143.7 MB
  • 20. Ensembling/17. Case Study Part2.mp4 143.3 MB
  • 26. Project Kaggle/17. Building Machine Learning model part2.mp4 141.7 MB
  • 10. Multiple Linear Regression/9. Case Study Part4.mp4 138.6 MB
  • 2. Numpy/2. Numpy Operations Part1.mp4 135.0 MB
  • 19. Decision Tree/9. DT Case Study Part1.mp4 131.5 MB
  • 15. Model Selection Part1/3. Gridsearch Case study Part1.mp4 130.3 MB
  • 26. Project Kaggle/16. Building Machine Learning model part1.mp4 130.0 MB
  • 23. Dimension Reduction/5. Case Study Part2.mp4 129.0 MB
  • 26. Project Kaggle/5. Train, Test And Cross Validation Split.mp4 121.9 MB
  • 14. Model Performance Metrics/1. Performance Metrics Part1.mp4 119.4 MB
  • 7. Data Visualisation/3. Case Study.mp4 118.7 MB
  • 26. Project Kaggle/3. Translating the Problem In Machine Learning World.mp4 118.5 MB
  • 1. Python Fundamentals/5. Variables in Python.mp4 115.8 MB
  • 24. Advanced Machine Learning Algorithms/8. Case Study.mp4 111.4 MB
  • 1. Python Fundamentals/11. String Part1.mp4 111.2 MB
  • 21. Model Selection Part2/1. Model Selection Part1.mp4 109.4 MB
  • 25. Deep Learning/6. Neural Network Playground.mp4 108.7 MB
  • 10. Multiple Linear Regression/3. Case Study part2.mp4 103.2 MB
  • 23. Dimension Reduction/2. PCA.mp4 103.2 MB
  • 26. Project Kaggle/4. Dealing with Text Data.mp4 102.8 MB
  • 23. Dimension Reduction/3. Maths Behind PCA.mp4 101.5 MB
  • 22. Unsupervised Learning/9. Case Study Part1.mp4 100.5 MB
  • 19. Decision Tree/10. DT Case Study Part2.mp4 100.4 MB
  • 16. Naive Bayes/9. Case Study 1.mp4 100.1 MB
  • 4. Some Fun With Maths/2. Linear Algebra Matrix Part1.mp4 99.9 MB
  • 1. Python Fundamentals/1. Introduction to the course.mp4 98.4 MB
  • 26. Project Kaggle/1. Introduction to the Problem Statement.mp4 97.9 MB
  • 14. Model Performance Metrics/2. Performance Metrics Part2.mp4 94.9 MB
  • 1. Python Fundamentals/2. Introduction to Kaggle.mp4 94.4 MB
  • 18. Support Vector Machine (SVM)/11. Case Study 2.mp4 94.4 MB
  • 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/4. Building Model Part2.mp4 92.1 MB
  • 1. Python Fundamentals/14. List Part2.mp4 91.6 MB
  • 1. Python Fundamentals/10. Functions.mp4 89.8 MB
  • 26. Project Kaggle/6. Understanding Evaluation Matrix Log Loss.mp4 89.7 MB
  • 13. KNN/11. Classification Case1.mp4 88.3 MB
  • 10. Multiple Linear Regression/2. Case Study part1.mp4 87.1 MB
  • 8. Exploratory Data Analysis/10. Univariate Analysis Part1.mp4 86.8 MB
  • 1. Python Fundamentals/3. Installation of Python and Anaconda.mp4 86.3 MB
  • 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/2. Playing With Data.mp4 85.3 MB
  • 16. Naive Bayes/3. Practical Example from NB with One Column.mp4 84.5 MB
  • 4. Some Fun With Maths/3. Linear Algebra Matrix Part2.mp4 81.8 MB
  • 1. Python Fundamentals/9. for while Loop.mp4 81.6 MB
  • 8. Exploratory Data Analysis/8. Data Cleaning part1.mp4 79.9 MB
  • 20. Ensembling/18. Case Study Part3.mp4 79.1 MB
  • 16. Naive Bayes/10. Case Study 2 Part1.mp4 78.2 MB
  • 18. Support Vector Machine (SVM)/7. SVM Case Study Part1.mp4 77.7 MB
  • 1. Python Fundamentals/15. List Part3.mp4 77.1 MB
  • 16. Naive Bayes/1. Introduction to Naive Bayes.mp4 76.9 MB
  • 20. Ensembling/5. Case study.mp4 76.6 MB
  • 10. Multiple Linear Regression/7. Case Study Part2.mp4 76.4 MB
  • 26. Project Kaggle/12. Significance of first categorical column.mp4 75.2 MB
  • 12. Gradient Descent/8. Gradient Descent case study.mp4 75.1 MB
  • 20. Ensembling/2. Bagging.mp4 74.7 MB
  • 18. Support Vector Machine (SVM)/10. Kernel Part2.mp4 74.6 MB
  • 26. Project Kaggle/9. First Categorical column analysis.mp4 74.6 MB
  • 13. KNN/10. Case Study.mp4 74.1 MB
  • 1. Python Fundamentals/20. Comprehentions.mp4 74.0 MB
  • 10. Multiple Linear Regression/4. Case Study part3.mp4 72.0 MB
  • 10. Multiple Linear Regression/6. Case Study Part1.mp4 71.9 MB
  • 26. Project Kaggle/7. Building A Worst Model.mp4 71.8 MB
  • 1. Python Fundamentals/17. Tuples.mp4 70.6 MB
  • 26. Project Kaggle/14. Third Categorical column.mp4 70.0 MB
  • 10. Multiple Linear Regression/8. Case Study Part3.mp4 69.8 MB
  • 3. Pandas/3. DataFrame.mp4 69.4 MB
  • 18. Support Vector Machine (SVM)/8. SVM Case Study Part2.mp4 69.4 MB
  • 18. Support Vector Machine (SVM)/3. Hyperplane Part2.mp4 68.5 MB
  • 24. Advanced Machine Learning Algorithms/4. Optimal Solution.mp4 68.4 MB
  • 10. Multiple Linear Regression/11. Case Study Part6 (RFE).mp4 67.3 MB
  • 1. Python Fundamentals/8. If else Loop.mp4 67.1 MB
  • 1. Python Fundamentals/16. List Part4.mp4 67.0 MB
  • 25. Deep Learning/5. Multi Layered Perceptron.mp4 66.9 MB
  • 16. Naive Bayes/2. Bayes Theorem.mp4 66.1 MB
  • 25. Deep Learning/3. History.mp4 64.9 MB
  • 1. Python Fundamentals/19. Dictionaries.mp4 64.6 MB
  • 3. Pandas/2. Series.mp4 64.5 MB
  • 22. Unsupervised Learning/10. Case Study Part2.mp4 64.3 MB
  • 18. Support Vector Machine (SVM)/13. Case Study 3 Part2.mp4 64.3 MB
  • 12. Gradient Descent/1. Pre-Req For Gradient Descent Part1.mp4 64.2 MB
  • 8. Exploratory Data Analysis/11. Univariate Analysis Part2.mp4 63.8 MB
  • 8. Exploratory Data Analysis/13. Bivariate Analysis.mp4 63.5 MB
  • 26. Project Kaggle/1.1 training.zip.zip 62.9 MB
  • 16. Naive Bayes/4. Practical Example from NB with Multiple Columns.mp4 62.7 MB
  • 3. Pandas/7. loc and iloc.mp4 62.3 MB
  • 22. Unsupervised Learning/1. Introduction to Clustering.mp4 62.0 MB
  • 26. Project Kaggle/8. Evaluating Worst ML Model.mp4 61.7 MB
  • 18. Support Vector Machine (SVM)/1. Introduction.mp4 61.6 MB
  • 9. Simple Linear Regression/4. How LR Works.mp4 61.5 MB
  • 6. Hypothesis Testing/6. z Table.mp4 61.5 MB
  • 1. Python Fundamentals/18. Sets.mp4 61.0 MB
  • 22. Unsupervised Learning/3. Kmeans.mp4 60.5 MB
  • 13. KNN/4. Accuracy of KNN.mp4 59.9 MB
  • 18. Support Vector Machine (SVM)/12. Case Study 3 Part1.mp4 58.7 MB
  • 16. Naive Bayes/7. Laplace Smoothing.mp4 57.9 MB
  • 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/3. Building Model Part1.mp4 57.7 MB
  • 5. Inferential Statistics/2. Probability Theory.mp4 57.4 MB
  • 16. Naive Bayes/5. Naive Bayes On Text Data Part1.mp4 57.4 MB
  • 26. Project Kaggle/10. Response encoding and one hot encoder.mp4 57.3 MB
  • 13. KNN/1. Introduction to Classification.mp4 56.7 MB
  • 7. Data Visualisation/4. Seaborn On Time Series Data.mp4 56.7 MB
  • 22. Unsupervised Learning/4. Maths Behind Kmeans.mp4 56.4 MB
  • 8. Exploratory Data Analysis/7. Data Sourcing and Cleaning part6.mp4 56.3 MB
  • 20. Ensembling/11. Adaboost Case Study.mp4 56.3 MB
  • 9. Simple Linear Regression/8. LR Case Study Part2.mp4 56.0 MB
  • 13. KNN/13. Classification Case3.mp4 55.5 MB
  • 9. Simple Linear Regression/5. Some Fun With Maths Behind LR.mp4 55.3 MB
  • 9. Simple Linear Regression/6. R Square.mp4 55.0 MB
  • 13. KNN/12. Classification Case2.mp4 54.8 MB
  • 15. Model Selection Part1/1. Model Creation Case1.mp4 54.6 MB
  • 22. Unsupervised Learning/6. Kmeans plus.mp4 54.3 MB
  • 26. Project Kaggle/21. Building Machine Learning model part6.mp4 53.3 MB
  • 26. Project Kaggle/15. Data pre-processing before building machine learning model.mp4 53.0 MB
  • 3. Pandas/6. Indexes.mp4 52.5 MB
  • 18. Support Vector Machine (SVM)/9. Kernel Part1.mp4 51.6 MB
  • 25. Deep Learning/2. Introduction.mp4 51.1 MB
  • 24. Advanced Machine Learning Algorithms/6. Regularization.mp4 51.0 MB
  • 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/5. Building Model Part3.mp4 50.9 MB
  • 26. Project Kaggle/11. Laplace Smoothing and Calibrated classifier.mp4 50.6 MB
  • 13. KNN/5. Effectiveness of KNN.mp4 50.6 MB
  • 13. KNN/6. Distance Metrics.mp4 50.2 MB
  • 13. KNN/3. Introduction to KNN.mp4 49.4 MB
  • 3. Pandas/10. groupby.mp4 49.2 MB
  • 9. Simple Linear Regression/9. LR Case Study Part3.mp4 48.7 MB
  • 16. Naive Bayes/6. Naive Bayes On Text Data Part2.mp4 48.3 MB
  • 10. Multiple Linear Regression/10. Case Study Part5.mp4 47.9 MB
  • 26. Project Kaggle/13. Second Categorical column.mp4 47.9 MB
  • 23. Dimension Reduction/4. Case Study Part1.mp4 47.7 MB
  • 24. Advanced Machine Learning Algorithms/3. Example Part2.mp4 47.3 MB
  • 17. Logistic Regression/2. Sigmoid Function.mp4 46.5 MB
  • 19. Decision Tree/4. Gini Index.mp4 46.3 MB
  • 3. Pandas/5. Operations Part2.mp4 46.2 MB
  • 3. Pandas/8. Reading CSV.mp4 44.5 MB
  • 26. Project Kaggle/20. Building Machine Learning model part5.mp4 44.0 MB
  • 8. Exploratory Data Analysis/14. Derived Columns.mp4 43.9 MB
  • 17. Logistic Regression/3. Log Odds.mp4 43.9 MB
  • 20. Ensembling/9. Adaboost Part1.mp4 43.6 MB
  • 21. Model Selection Part2/2. Model Selection Part2.mp4 43.3 MB
  • 13. KNN/14. Classification Case4.mp4 43.1 MB
  • 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/1. Introduction to the Problem Statement.mp4 42.8 MB
  • 19. Decision Tree/2. Example of DT.mp4 42.6 MB
  • 19. Decision Tree/8. Preventing Overfitting Issues in DT.mp4 42.2 MB
  • 13. KNN/2. Defining Classification Mathematically.mp4 41.9 MB
  • 24. Advanced Machine Learning Algorithms/5. Case study.mp4 41.9 MB
  • 24. Advanced Machine Learning Algorithms/7. Ridge and Lasso.mp4 41.9 MB
  • 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/6. Verification of Model.mp4 41.4 MB
  • 20. Ensembling/1. Introduction to Ensembles.mp4 41.2 MB
  • 3. Pandas/1. Introduction.mp4 41.0 MB
  • 20. Ensembling/15. XGboost Algorithm.mp4 40.6 MB
  • 5. Inferential Statistics/12. Sampling.mp4 40.6 MB
  • 20. Ensembling/10. Adaboost Part2.mp4 40.3 MB
  • 26. Project Kaggle/18. Building Machine Learning model part3.mp4 40.3 MB
  • 22. Unsupervised Learning/12. Hierarchial Clustering.mp4 39.9 MB
  • 6. Hypothesis Testing/4. OneTwo Tailed Tests.mp4 39.8 MB
  • 12. Gradient Descent/5. Gradient Descent.mp4 39.5 MB
  • 1. Python Fundamentals/6. Numeric Operations in Python.mp4 38.7 MB
  • 12. Gradient Descent/4. Defining Cost Functions More Formally.mp4 38.3 MB
  • 22. Unsupervised Learning/7. Value of K.mp4 37.6 MB
  • 21. Model Selection Part2/3. Model Selection Part3.mp4 37.4 MB
  • 20. Ensembling/14. Boosting Part2.mp4 37.2 MB
  • 9. Simple Linear Regression/2. Types of Machine Learning.mp4 37.1 MB
  • 15. Model Selection Part1/2. Model Creation Case2.mp4 36.4 MB
  • 5. Inferential Statistics/15. Confidence Interval Part1.mp4 36.2 MB
  • 22. Unsupervised Learning/13. Case Study.mp4 36.1 MB
  • 3. Pandas/11. Merging Part2.mp4 35.6 MB
  • 6. Hypothesis Testing/9. p Value.mp4 35.1 MB
  • 13. KNN/8. Finding k.mp4 34.9 MB
  • 18. Support Vector Machine (SVM)/6. Slack Variable.mp4 34.9 MB
  • 26. Project Kaggle/19. Building Machine Learning model part4.mp4 34.7 MB
  • 20. Ensembling/6. Introduction to Boosting.mp4 34.7 MB
  • 12. Gradient Descent/2. Pre-Req For Gradient Descent Part2.mp4 34.5 MB
  • 24. Advanced Machine Learning Algorithms/9. Model Selection.mp4 32.8 MB
  • 6. Hypothesis Testing/1. Introduction.mp4 32.6 MB
  • 24. Advanced Machine Learning Algorithms/1. Introduction.mp4 32.4 MB
  • 3. Pandas/9. Merging Part1.mp4 31.5 MB
  • 25. Deep Learning/4. Perceptron.mp4 31.2 MB
  • 19. Decision Tree/1. Introduction.mp4 31.2 MB
  • 8. Exploratory Data Analysis/9. Data Cleaning part2.mp4 31.1 MB
  • 6. Hypothesis Testing/12. t- distribution Part2.mp4 30.7 MB
  • 19. Decision Tree/5. Information Gain Part1.mp4 30.7 MB
  • 13. KNN/7. Distance Metrics Part2.mp4 30.2 MB
  • 6. Hypothesis Testing/2. NULL And Alternate Hypothesis.mp4 30.2 MB
  • 5. Inferential Statistics/6. Without Experiment.mp4 30.1 MB
  • 22. Unsupervised Learning/2. Segmentation.mp4 30.0 MB
  • 6. Hypothesis Testing/3. Examples.mp4 29.1 MB
  • 4. Some Fun With Maths/4. Linear Algebra Going From 2D to nD Part1.mp4 29.1 MB
  • 3. Pandas/12. Pivot Table.mp4 29.1 MB
  • 24. Advanced Machine Learning Algorithms/2. Example Part1.mp4 28.8 MB
  • 1. Python Fundamentals/12. String Part2.mp4 28.7 MB
  • 19. Decision Tree/6. Information Gain Part2.mp4 28.7 MB
  • 16. Naive Bayes/8. Bernoulli Naive Bayes.mp4 28.4 MB
  • 18. Support Vector Machine (SVM)/2. Hyperplane Part1.mp4 28.4 MB
  • 12. Gradient Descent/7. Closed Form Vs Gradient Descent.mp4 27.9 MB
  • 17. Logistic Regression/1. Introduction.mp4 27.9 MB
  • 6. Hypothesis Testing/7. Examples.mp4 27.7 MB
  • 4. Some Fun With Maths/5. Linear Algebra 2D to nD Part2.mp4 27.0 MB
  • 5. Inferential Statistics/13. Sampling Distribution.mp4 26.8 MB
  • 16. Naive Bayes/11. Case Study 2 Part2.mp4 26.6 MB
  • 2. Numpy/1. Introduction.mp4 25.9 MB
  • 6. Hypothesis Testing/5. Critical Value Method.mp4 25.9 MB
  • 8. Exploratory Data Analysis/12. Segmented Analysis.mp4 25.7 MB
  • 5. Inferential Statistics/4. Expected Values Part1.mp4 25.4 MB
  • 5. Inferential Statistics/3. Probability Distribution.mp4 25.4 MB
  • 18. Support Vector Machine (SVM)/4. Maths Behind SVM.mp4 25.2 MB
  • 14. Model Performance Metrics/3. Performance Metrics Part3.mp4 25.2 MB
  • 5. Inferential Statistics/11. z Score.mp4 25.0 MB
  • 20. Ensembling/12. XGBoost.mp4 24.2 MB
  • 12. Gradient Descent/6. Optimisation.mp4 22.7 MB
  • 6. Hypothesis Testing/11. t- distribution Part1.mp4 22.4 MB
  • 5. Inferential Statistics/9. PDF.mp4 22.0 MB
  • 19. Decision Tree/3. Homogenity.mp4 21.6 MB
  • 24. Advanced Machine Learning Algorithms/10. Adjusted R Square.mp4 21.1 MB
  • 5. Inferential Statistics/10. Normal Distribution.mp4 19.9 MB
  • 22. Unsupervised Learning/11. More on Segmentation.mp4 18.9 MB
  • 20. Ensembling/7. Weak Learners.mp4 18.8 MB
  • 9. Simple Linear Regression/3. Introduction to Linear Regression (LR).mp4 18.8 MB
  • 5. Inferential Statistics/7. Binomial Distribution.mp4 18.4 MB
  • 1. Python Fundamentals/7. Logical Operations.mp4 18.2 MB
  • 6. Hypothesis Testing/8. More Examples.mp4 17.3 MB
  • 10. Multiple Linear Regression/1. Introduction.mp4 17.3 MB
  • 20. Ensembling/4. Runtime.mp4 17.2 MB
  • 8. Exploratory Data Analysis/3. Data Sourcing and Cleaning part2.mp4 16.4 MB
  • 8. Exploratory Data Analysis/2. Data Sourcing and Cleaning part1.mp4 16.3 MB
  • 19. Decision Tree/7. Advantages and Disadvantages of DT.mp4 16.2 MB
  • 18. Support Vector Machine (SVM)/1.1 SVM.zip.zip 16.2 MB
  • 6. Hypothesis Testing/10. Types of Error.mp4 16.0 MB
  • 20. Ensembling/8. Shallow Decision Tree.mp4 15.7 MB
  • 20. Ensembling/3. Advantages.mp4 15.6 MB
  • 5. Inferential Statistics/5. Expected Values Part2.mp4 15.2 MB
  • 20. Ensembling/13. Boosting Part1.mp4 14.4 MB
  • 5. Inferential Statistics/16. Confidence Interval Part2.mp4 14.0 MB
  • 12. Gradient Descent/3. Cost Functions.mp4 13.8 MB
  • 5. Inferential Statistics/14. Central Limit Theorem.mp4 13.7 MB
  • 8. Exploratory Data Analysis/6. Data Sourcing and Cleaning part5.mp4 13.0 MB
  • 22. Unsupervised Learning/8. Hopkins test.mp4 12.9 MB
  • 3. Pandas/4. Operations Part1.mp4 12.6 MB
  • 9. Simple Linear Regression/1. Introduction to Machine Learning.mp4 11.7 MB
  • 18. Support Vector Machine (SVM)/5. Support Vectors.mp4 11.6 MB
  • 8. Exploratory Data Analysis/5. Data Sourcing and Cleaning part4.mp4 10.9 MB
  • 5. Inferential Statistics/1. Inferential Statistics.mp4 10.8 MB
  • 1. Python Fundamentals/4. Python Introduction.mp4 10.7 MB
  • 1. Python Fundamentals/13. List Part1.mp4 10.5 MB
  • 8. Exploratory Data Analysis/4. Data Sourcing and Cleaning part3.mp4 10.5 MB
  • 22. Unsupervised Learning/5. More Maths.mp4 9.9 MB
  • 25. Deep Learning/1. Expectations.mp4 9.8 MB
  • 13. KNN/9. KNN on Regression.mp4 9.7 MB
  • 23. Dimension Reduction/1.1 PCA code for udemy.zip.zip 9.5 MB
  • 5. Inferential Statistics/8. Commulative Distribution.mp4 8.8 MB
  • 10. Multiple Linear Regression/5. Adjusted R Square.mp4 8.5 MB
  • 22. Unsupervised Learning/1.1 Unsupervised.zip.zip 7.7 MB
  • 9. Simple Linear Regression/10. Residual Square Error (RSE).mp4 4.8 MB
  • 19. Decision Tree/1.1 DT_forudemy.zip.zip 4.2 MB
  • 8. Exploratory Data Analysis/1. Introduction.mp4 4.0 MB
  • 1. Python Fundamentals/3.2 Installing-Python.Teclov.pdf.pdf 1.4 MB
  • 13. KNN/1.1 KNN.zip.zip 1.4 MB
  • 26. Project Kaggle/1.2 Teclov Project - Medical treatment.ipynb.zip.zip 1.3 MB
  • 20. Ensembling/1.1 Boosting.zip.zip 1.3 MB
  • 7. Data Visualisation/1.1 Datavisual.zip.zip 1.3 MB
  • 24. Advanced Machine Learning Algorithms/1.1 AdvanceReg.zip.zip 1.2 MB
  • 20. Ensembling/1.2 RF_forudemy.zip.zip 1.1 MB
  • 17. Logistic Regression/1.1 LogisticReg.zip.zip 1.0 MB
  • 10. Multiple Linear Regression/1.1 Multplr_LR_Code_for Udemy.zip.zip 533.5 kB
  • 15. Model Selection Part1/1.1 CrossValidation_Linear Regression.zip.zip 350.4 kB
  • 16. Naive Bayes/1.1 NaiveBayes.zip.zip 272.4 kB
  • 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/1.1 Hotstarcode-for-udemy.zip.zip 260.7 kB
  • 12. Gradient Descent/1.1 Gradient+Descent+Updated.zip.zip 165.0 kB
  • 6. Hypothesis Testing/1.2 t-table.pdf.pdf 150.8 kB
  • 9. Simple Linear Regression/1.1 code-LR-Teclov.zip.zip 78.7 kB
  • 6. Hypothesis Testing/1.1 z-table.pdf.pdf 60.4 kB
  • 4. Some Fun With Maths/1. Linear Algebra Vectors.vtt 51.1 kB
  • 23. Dimension Reduction/1. Introduction.vtt 32.9 kB
  • 2. Numpy/3. Numpy Operations Part2.vtt 30.5 kB
  • 14. Model Performance Metrics/1. Performance Metrics Part1.vtt 27.8 kB
  • 23. Dimension Reduction/2. PCA.vtt 27.1 kB
  • 7. Data Visualisation/1. Matplotlib.vtt 27.0 kB
  • 7. Data Visualisation/2. Seaborn.vtt 26.6 kB
  • 8. Exploratory Data Analysis/10. Univariate Analysis Part1.vtt 26.4 kB
  • 23. Dimension Reduction/3. Maths Behind PCA.vtt 26.1 kB
  • 13. KNN/11. Classification Case1.vtt 25.6 kB
  • 2. Numpy/2. Numpy Operations Part1.vtt 24.4 kB
  • 21. Model Selection Part2/1. Model Selection Part1.vtt 23.8 kB
  • 1. Python Fundamentals/5. Variables in Python.vtt 21.2 kB
  • 17. Logistic Regression/4. Case Study.vtt 20.9 kB
  • 18. Support Vector Machine (SVM)/14. Case Study 4.vtt 20.9 kB
  • 26. Project Kaggle/6. Understanding Evaluation Matrix Log Loss.vtt 20.5 kB
  • 8. Exploratory Data Analysis/11. Univariate Analysis Part2.vtt 20.0 kB
  • 4. Some Fun With Maths/3. Linear Algebra Matrix Part2.vtt 19.9 kB
  • 14. Model Performance Metrics/2. Performance Metrics Part2.vtt 19.6 kB
  • 23. Dimension Reduction/5. Case Study Part2.vtt 19.4 kB
  • 15. Model Selection Part1/4. Gridsearch Case study Part2.vtt 18.9 kB
  • 25. Deep Learning/3. History.vtt 18.4 kB
  • 26. Project Kaggle/2. Playing With The Data.vtt 18.3 kB
  • 10. Multiple Linear Regression/9. Case Study Part4.vtt 18.2 kB
  • 16. Naive Bayes/1. Introduction to Naive Bayes.vtt 18.2 kB
  • 12. Gradient Descent/1. Pre-Req For Gradient Descent Part1.vtt 18.0 kB
  • 9. Simple Linear Regression/7. LR Case Study Part1.vtt 17.9 kB
  • 26. Project Kaggle/16. Building Machine Learning model part1.vtt 17.7 kB
  • 13. KNN/12. Classification Case2.vtt 17.3 kB
  • 4. Some Fun With Maths/2. Linear Algebra Matrix Part1.vtt 17.3 kB
  • 18. Support Vector Machine (SVM)/3. Hyperplane Part2.vtt 17.2 kB
  • 24. Advanced Machine Learning Algorithms/4. Optimal Solution.vtt 17.1 kB
  • 8. Exploratory Data Analysis/8. Data Cleaning part1.vtt 17.1 kB
  • 8. Exploratory Data Analysis/13. Bivariate Analysis.vtt 16.8 kB
  • 1. Python Fundamentals/3.1 Python-code-udemy.zip.zip 16.8 kB
  • 1. Python Fundamentals/4.1 Python-code-udemy.zip.zip 16.8 kB
  • 1. Python Fundamentals/1. Introduction to the course.vtt 16.7 kB
  • 13. KNN/5. Effectiveness of KNN.vtt 16.2 kB
  • 1. Python Fundamentals/11. String Part1.vtt 15.9 kB
  • 13. KNN/1. Introduction to Classification.vtt 15.9 kB
  • 3. Pandas/1.1 Pandas.zip.zip 15.8 kB
  • 20. Ensembling/2. Bagging.vtt 15.8 kB
  • 26. Project Kaggle/17. Building Machine Learning model part2.vtt 15.7 kB
  • 13. KNN/13. Classification Case3.vtt 15.6 kB
  • 13. KNN/4. Accuracy of KNN.vtt 15.2 kB
  • 26. Project Kaggle/9. First Categorical column analysis.vtt 15.0 kB
  • 13. KNN/6. Distance Metrics.vtt 15.0 kB
  • 21. Model Selection Part2/2. Model Selection Part2.vtt 14.9 kB
  • 1. Python Fundamentals/10. Functions.vtt 14.8 kB
  • 25. Deep Learning/5. Multi Layered Perceptron.vtt 14.8 kB
  • 26. Project Kaggle/11. Laplace Smoothing and Calibrated classifier.vtt 14.8 kB
  • 8. Exploratory Data Analysis/14. Derived Columns.vtt 14.8 kB
  • 5. Inferential Statistics/2. Probability Theory.vtt 14.2 kB
  • 13. KNN/14. Classification Case4.vtt 14.1 kB
  • 18. Support Vector Machine (SVM)/1. Introduction.vtt 14.0 kB
  • 13. KNN/3. Introduction to KNN.vtt 14.0 kB
  • 25. Deep Learning/6. Neural Network Playground.vtt 13.9 kB
  • 15. Model Selection Part1/3. Gridsearch Case study Part1.vtt 13.8 kB
  • 20. Ensembling/17. Case Study Part2.vtt 13.7 kB
  • 22. Unsupervised Learning/4. Maths Behind Kmeans.vtt 13.5 kB
  • 22. Unsupervised Learning/9. Case Study Part1.vtt 13.5 kB
  • 1. Python Fundamentals/14. List Part2.vtt 13.5 kB
  • 16. Naive Bayes/4. Practical Example from NB with Multiple Columns.vtt 13.5 kB
  • 1. Python Fundamentals/9. for while Loop.vtt 13.3 kB
  • 19. Decision Tree/9. DT Case Study Part1.vtt 13.2 kB
  • 7. Data Visualisation/3. Case Study.vtt 13.2 kB
  • 16. Naive Bayes/2. Bayes Theorem.vtt 13.0 kB
  • 22. Unsupervised Learning/1. Introduction to Clustering.vtt 13.0 kB
  • 18. Support Vector Machine (SVM)/10. Kernel Part2.vtt 13.0 kB
  • 12. Gradient Descent/5. Gradient Descent.vtt 12.8 kB
  • 15. Model Selection Part1/1. Model Creation Case1.vtt 12.7 kB
  • 9. Simple Linear Regression/6. R Square.vtt 12.6 kB
  • 10. Multiple Linear Regression/7. Case Study Part2.vtt 12.6 kB
  • 26. Project Kaggle/5. Train, Test And Cross Validation Split.vtt 12.6 kB
  • 10. Multiple Linear Regression/3. Case Study part2.vtt 12.5 kB
  • 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/2. Playing With Data.vtt 12.2 kB
  • 26. Project Kaggle/3. Translating the Problem In Machine Learning World.vtt 12.2 kB
  • 19. Decision Tree/8. Preventing Overfitting Issues in DT.vtt 12.1 kB
  • 20. Ensembling/16. Case Study Part1.vtt 12.1 kB
  • 17. Logistic Regression/2. Sigmoid Function.vtt 11.9 kB
  • 13. KNN/8. Finding k.vtt 11.8 kB
  • 22. Unsupervised Learning/6. Kmeans plus.vtt 11.7 kB
  • 1. Python Fundamentals/2. Introduction to Kaggle.vtt 11.4 kB
  • 1. Python Fundamentals/3. Installation of Python and Anaconda.vtt 11.4 kB
  • 20. Ensembling/1. Introduction to Ensembles.vtt 11.3 kB
  • 8. Exploratory Data Analysis/9. Data Cleaning part2.vtt 11.3 kB
  • 9. Simple Linear Regression/5. Some Fun With Maths Behind LR.vtt 11.2 kB
  • 17. Logistic Regression/3. Log Odds.vtt 11.2 kB
  • 19. Decision Tree/10. DT Case Study Part2.vtt 11.2 kB
  • 16. Naive Bayes/9. Case Study 1.vtt 11.1 kB
  • 13. KNN/10. Case Study.vtt 11.1 kB
  • 24. Advanced Machine Learning Algorithms/3. Example Part2.vtt 11.0 kB
  • 24. Advanced Machine Learning Algorithms/8. Case Study.vtt 10.9 kB
  • 16. Naive Bayes/3. Practical Example from NB with One Column.vtt 10.9 kB
  • 26. Project Kaggle/7. Building A Worst Model.vtt 10.9 kB
  • 25. Deep Learning/2. Introduction.vtt 10.8 kB
  • 1. Python Fundamentals/15. List Part3.vtt 10.7 kB
  • 1. Python Fundamentals/16. List Part4.vtt 10.7 kB
  • 24. Advanced Machine Learning Algorithms/6. Regularization.vtt 10.6 kB
  • 18. Support Vector Machine (SVM)/6. Slack Variable.vtt 10.5 kB
  • 1. Python Fundamentals/17. Tuples.vtt 10.4 kB
  • 6. Hypothesis Testing/4. OneTwo Tailed Tests.vtt 10.4 kB
  • 22. Unsupervised Learning/3. Kmeans.vtt 10.4 kB
  • 1. Python Fundamentals/8. If else Loop.vtt 10.3 kB
  • 16. Naive Bayes/5. Naive Bayes On Text Data Part1.vtt 10.2 kB
  • 4. Some Fun With Maths/4. Linear Algebra Going From 2D to nD Part1.vtt 10.2 kB
  • 9. Simple Linear Regression/4. How LR Works.vtt 10.2 kB
  • 18. Support Vector Machine (SVM)/12. Case Study 3 Part1.vtt 10.2 kB
  • 26. Project Kaggle/4. Dealing with Text Data.vtt 10.1 kB
  • 5. Inferential Statistics/12. Sampling.vtt 10.1 kB
  • 26. Project Kaggle/1. Introduction to the Problem Statement.vtt 9.9 kB
  • 3. Pandas/2. Series.vtt 9.8 kB
  • 18. Support Vector Machine (SVM)/9. Kernel Part1.vtt 9.6 kB
  • 3. Pandas/7. loc and iloc.vtt 9.6 kB
  • 3. Pandas/3. DataFrame.vtt 9.5 kB
  • 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/4. Building Model Part2.vtt 9.5 kB
  • 13. KNN/7. Distance Metrics Part2.vtt 9.5 kB
  • 6. Hypothesis Testing/1. Introduction.vtt 9.4 kB
  • 19. Decision Tree/2. Example of DT.vtt 9.4 kB
  • 26. Project Kaggle/21. Building Machine Learning model part6.vtt 9.3 kB
  • 22. Unsupervised Learning/10. Case Study Part2.vtt 9.3 kB
  • 22. Unsupervised Learning/12. Hierarchial Clustering.vtt 9.2 kB
  • 13. KNN/2. Defining Classification Mathematically.vtt 9.2 kB
  • 9. Simple Linear Regression/2. Types of Machine Learning.vtt 9.2 kB
  • 19. Decision Tree/1. Introduction.vtt 9.2 kB
  • 12. Gradient Descent/2. Pre-Req For Gradient Descent Part2.vtt 9.2 kB
  • 26. Project Kaggle/12. Significance of first categorical column.vtt 9.1 kB
  • 16. Naive Bayes/10. Case Study 2 Part1.vtt 9.1 kB
  • 19. Decision Tree/4. Gini Index.vtt 9.1 kB
  • 10. Multiple Linear Regression/6. Case Study Part1.vtt 9.0 kB
  • 6. Hypothesis Testing/6. z Table.vtt 9.0 kB
  • 20. Ensembling/15. XGboost Algorithm.vtt 9.0 kB
  • 15. Model Selection Part1/2. Model Creation Case2.vtt 9.0 kB
  • 22. Unsupervised Learning/2. Segmentation.vtt 8.9 kB
  • 12. Gradient Descent/4. Defining Cost Functions More Formally.vtt 8.8 kB
  • 26. Project Kaggle/14. Third Categorical column.vtt 8.8 kB
  • 10. Multiple Linear Regression/2. Case Study part1.vtt 8.7 kB
  • 18. Support Vector Machine (SVM)/8. SVM Case Study Part2.vtt 8.6 kB
  • 18. Support Vector Machine (SVM)/11. Case Study 2.vtt 8.6 kB
  • 1. Python Fundamentals/19. Dictionaries.vtt 8.5 kB
  • 20. Ensembling/9. Adaboost Part1.vtt 8.5 kB
  • 25. Deep Learning/4. Perceptron.vtt 8.4 kB
  • 10. Multiple Linear Regression/11. Case Study Part6 (RFE).vtt 8.4 kB
  • 17. Logistic Regression/1. Introduction.vtt 8.4 kB
  • 4. Some Fun With Maths/5. Linear Algebra 2D to nD Part2.vtt 8.4 kB
  • 18. Support Vector Machine (SVM)/4. Maths Behind SVM.vtt 8.3 kB
  • 1. Python Fundamentals/20. Comprehentions.vtt 8.3 kB
  • 20. Ensembling/10. Adaboost Part2.vtt 8.1 kB
  • 3. Pandas/1. Introduction.vtt 8.1 kB
  • 20. Ensembling/14. Boosting Part2.vtt 8.0 kB
  • 1. Python Fundamentals/18. Sets.vtt 8.0 kB
  • 8. Exploratory Data Analysis/12. Segmented Analysis.vtt 8.0 kB
  • 10. Multiple Linear Regression/4. Case Study part3.vtt 7.9 kB
  • 24. Advanced Machine Learning Algorithms/7. Ridge and Lasso.vtt 7.9 kB
  • 10. Multiple Linear Regression/8. Case Study Part3.vtt 7.8 kB
  • 22. Unsupervised Learning/7. Value of K.vtt 7.8 kB
  • 6. Hypothesis Testing/2. NULL And Alternate Hypothesis.vtt 7.7 kB
  • 3. Pandas/6. Indexes.vtt 7.6 kB
  • 5. Inferential Statistics/15. Confidence Interval Part1.vtt 7.4 kB
  • 5. Inferential Statistics/6. Without Experiment.vtt 7.4 kB
  • 24. Advanced Machine Learning Algorithms/1. Introduction.vtt 7.3 kB
  • 1. Python Fundamentals/6. Numeric Operations in Python.vtt 7.3 kB
  • 26. Project Kaggle/8. Evaluating Worst ML Model.vtt 7.2 kB
  • 3. Pandas/10. groupby.vtt 7.2 kB
  • 3. Pandas/8. Reading CSV.vtt 7.1 kB
  • 20. Ensembling/5. Case study.vtt 7.1 kB
  • 20. Ensembling/18. Case Study Part3.vtt 7.0 kB
  • 5. Inferential Statistics/13. Sampling Distribution.vtt 7.0 kB
  • 12. Gradient Descent/8. Gradient Descent case study.vtt 7.0 kB
  • 19. Decision Tree/5. Information Gain Part1.vtt 6.8 kB
  • 6. Hypothesis Testing/3. Examples.vtt 6.8 kB
  • 16. Naive Bayes/6. Naive Bayes On Text Data Part2.vtt 6.7 kB
  • 26. Project Kaggle/10. Response encoding and one hot encoder.vtt 6.7 kB
  • 22. Unsupervised Learning/13. Case Study.vtt 6.7 kB
  • 24. Advanced Machine Learning Algorithms/9. Model Selection.vtt 6.7 kB
  • 18. Support Vector Machine (SVM)/7. SVM Case Study Part1.vtt 6.6 kB
  • 20. Ensembling/6. Introduction to Boosting.vtt 6.6 kB
  • 6. Hypothesis Testing/9. p Value.vtt 6.6 kB
  • 2. Numpy/1. Introduction.vtt 6.4 kB
  • 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/1. Introduction to the Problem Statement.vtt 6.4 kB
  • 14. Model Performance Metrics/3. Performance Metrics Part3.vtt 6.4 kB
  • 18. Support Vector Machine (SVM)/13. Case Study 3 Part2.vtt 6.4 kB
  • 18. Support Vector Machine (SVM)/2. Hyperplane Part1.vtt 6.3 kB
  • 10. Multiple Linear Regression/10. Case Study Part5.vtt 6.2 kB
  • 3. Pandas/5. Operations Part2.vtt 6.2 kB
  • 23. Dimension Reduction/4. Case Study Part1.vtt 6.2 kB
  • 24. Advanced Machine Learning Algorithms/2. Example Part1.vtt 6.2 kB
  • 20. Ensembling/11. Adaboost Case Study.vtt 6.2 kB
  • 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/3. Building Model Part1.vtt 6.0 kB
  • 19. Decision Tree/3. Homogenity.vtt 6.0 kB
  • 12. Gradient Descent/7. Closed Form Vs Gradient Descent.vtt 5.9 kB
  • 3. Pandas/11. Merging Part2.vtt 5.9 kB
  • 19. Decision Tree/6. Information Gain Part2.vtt 5.8 kB
  • 26. Project Kaggle/15. Data pre-processing before building machine learning model.vtt 5.8 kB
  • 7. Data Visualisation/4. Seaborn On Time Series Data.vtt 5.7 kB
  • 9. Simple Linear Regression/9. LR Case Study Part3.vtt 5.7 kB
  • 5. Inferential Statistics/4. Expected Values Part1.vtt 5.6 kB
  • 22. Unsupervised Learning/11. More on Segmentation.vtt 5.6 kB
  • 5. Inferential Statistics/3. Probability Distribution.vtt 5.6 kB
  • 9. Simple Linear Regression/8. LR Case Study Part2.vtt 5.6 kB
  • 5. Inferential Statistics/9. PDF.vtt 5.6 kB
  • 5. Inferential Statistics/11. z Score.vtt 5.5 kB
  • 5. Inferential Statistics/10. Normal Distribution.vtt 5.4 kB
  • 26. Project Kaggle/13. Second Categorical column.vtt 5.4 kB
  • 2. Numpy/1.1 Teclov-numpy.ipynb.zip.zip 5.3 kB
  • 26. Project Kaggle/20. Building Machine Learning model part5.vtt 5.2 kB
  • 20. Ensembling/3. Advantages.vtt 5.2 kB
  • 12. Gradient Descent/6. Optimisation.vtt 5.2 kB
  • 16. Naive Bayes/7. Laplace Smoothing.vtt 5.1 kB
  • 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/6. Verification of Model.vtt 4.9 kB
  • 20. Ensembling/12. XGBoost.vtt 4.9 kB
  • 20. Ensembling/4. Runtime.vtt 4.8 kB
  • 11. HotstarNetflix Real world Case Study for Multiple Linear Regression/5. Building Model Part3.vtt 4.8 kB
  • 6. Hypothesis Testing/5. Critical Value Method.vtt 4.7 kB
  • 3. Pandas/12. Pivot Table.vtt 4.6 kB
  • 19. Decision Tree/7. Advantages and Disadvantages of DT.vtt 4.5 kB
  • 8. Exploratory Data Analysis/7. Data Sourcing and Cleaning part6.vtt 4.5 kB
  • 3. Pandas/9. Merging Part1.vtt 4.4 kB
  • 24. Advanced Machine Learning Algorithms/5. Case study.vtt 4.3 kB
  • 5. Inferential Statistics/7. Binomial Distribution.vtt 4.3 kB
  • 26. Project Kaggle/18. Building Machine Learning model part3.vtt 4.3 kB
  • 6. Hypothesis Testing/11. t- distribution Part1.vtt 4.2 kB
  • 24. Advanced Machine Learning Algorithms/10. Adjusted R Square.vtt 4.2 kB
  • 8. Exploratory Data Analysis/2. Data Sourcing and Cleaning part1.vtt 4.1 kB
  • 18. Support Vector Machine (SVM)/5. Support Vectors.vtt 4.0 kB
  • 26. Project Kaggle/19. Building Machine Learning model part4.vtt 4.0 kB
  • 5. Inferential Statistics/5. Expected Values Part2.vtt 4.0 kB
  • 8. Exploratory Data Analysis/5. Data Sourcing and Cleaning part4.vtt 3.9 kB
  • 8. Exploratory Data Analysis/6. Data Sourcing and Cleaning part5.vtt 3.8 kB
  • 20. Ensembling/13. Boosting Part1.vtt 3.8 kB
  • 10. Multiple Linear Regression/1. Introduction.vtt 3.7 kB
  • 6. Hypothesis Testing/7. Examples.vtt 3.6 kB
  • 1. Python Fundamentals/4. Python Introduction.vtt 3.6 kB
  • 1. Python Fundamentals/12. String Part2.vtt 3.5 kB
  • 6. Hypothesis Testing/10. Types of Error.vtt 3.5 kB
  • 6. Hypothesis Testing/8. More Examples.vtt 3.5 kB
  • 8. Exploratory Data Analysis/4. Data Sourcing and Cleaning part3.vtt 3.4 kB
  • 1. Python Fundamentals/7. Logical Operations.vtt 3.3 kB
  • 5. Inferential Statistics/16. Confidence Interval Part2.vtt 3.3 kB
  • 20. Ensembling/7. Weak Learners.vtt 3.2 kB
  • 6. Hypothesis Testing/12. t- distribution Part2.vtt 3.1 kB
  • 5. Inferential Statistics/1. Inferential Statistics.vtt 3.1 kB
  • 22. Unsupervised Learning/8. Hopkins test.vtt 3.1 kB
  • 5. Inferential Statistics/14. Central Limit Theorem.vtt 3.0 kB
  • 9. Simple Linear Regression/3. Introduction to Linear Regression (LR).vtt 3.0 kB
  • 16. Naive Bayes/11. Case Study 2 Part2.vtt 3.0 kB
  • 13. KNN/9. KNN on Regression.vtt 3.0 kB
  • 1. Python Fundamentals/13. List Part1.vtt 3.0 kB
  • 22. Unsupervised Learning/5. More Maths.vtt 3.0 kB
  • 12. Gradient Descent/3. Cost Functions.vtt 2.9 kB
  • 25. Deep Learning/1. Expectations.vtt 2.9 kB
  • 20. Ensembling/8. Shallow Decision Tree.vtt 2.8 kB
  • 5. Inferential Statistics/8. Commulative Distribution.vtt 2.8 kB
  • 8. Exploratory Data Analysis/3. Data Sourcing and Cleaning part2.vtt 2.6 kB
  • 9. Simple Linear Regression/1. Introduction to Machine Learning.vtt 2.2 kB
  • 16. Naive Bayes/8. Bernoulli Naive Bayes.vtt 2.1 kB
  • 3. Pandas/4. Operations Part1.vtt 1.5 kB
  • [FTU Forum].url 1.4 kB
  • 9. Simple Linear Regression/10. Residual Square Error (RSE).vtt 1.0 kB
  • 8. Exploratory Data Analysis/1. Introduction.vtt 897 Bytes
  • 10. Multiple Linear Regression/5. Adjusted R Square.vtt 855 Bytes
  • How you can help Team-FTU.txt 241 Bytes
  • [FreeCoursesOnline.Me].url 133 Bytes
  • [FreeTutorials.Eu].url 129 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！