Udemy - Data Science Methods and Techniques [2025] (7.2024)

磁力链接/BT种子名称

Udemy - Data Science Methods and Techniques [2025] (7.2024)

磁力链接/BT种子简介

种子哈希：00f760b86cbbbb163da912939cdedb21e94116d4
文件大小： 7.06G
已经下载：15次
下载速度：极快
收录时间：2025-08-10
最近下载：2025-08-31

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文件列表

  • 3. Classification and Supervised Learning/4. The Decision Tree Classifier.mp4 575.8 MB
  • 2. Regression, Prediction and Supervised Learning/10. Regression Regularization, Lasso and Ridge models (X).mp4 545.7 MB
  • 2. Regression, Prediction and Supervised Learning/11. Decision Tree Regression models (XI).mp4 499.2 MB
  • 2. Regression, Prediction and Supervised Learning/12. Random Forest Regression (XII).mp4 430.5 MB
  • 2. Regression, Prediction and Supervised Learning/9. Multivariate Polynomial Multiple Regression models (VIIII).mp4 428.7 MB
  • 3. Classification and Supervised Learning/2. Logistic Regression Classifier.mp4 398.1 MB
  • 2. Regression, Prediction and Supervised Learning/6. Linear Multiple Regression model (VI).mp4 354.7 MB
  • 4. Cluster Analysis and Unsupervised Learning/3. Density-Based Spatial Clustering of Applications with Noise (DBSCAN).mp4 350.6 MB
  • 3. Classification and Supervised Learning/5. The Random Forest Classifier.mp4 347.6 MB
  • 3. Classification and Supervised Learning/3. The Naive Bayes Classifier.mp4 344.2 MB
  • 2. Regression, Prediction and Supervised Learning/3. The Traditional Simple Regression Model (III).mp4 332.9 MB
  • 1. Introduction/4. The Conda Package Management System (optional).mp4 281.4 MB
  • 4. Cluster Analysis and Unsupervised Learning/2. K-Means Cluster Analysis, and an introduction to auto-updated K-means algorithms.mp4 266.0 MB
  • 2. Regression, Prediction and Supervised Learning/13. Voting Regression (XIII).mp4 256.8 MB
  • 2. Regression, Prediction and Supervised Learning/7. Linear Multiple Regression model (VII).mp4 244.3 MB
  • 4. Cluster Analysis and Unsupervised Learning/4. Four Hierarchical Clustering algorithms.mp4 220.9 MB
  • 3. Classification and Supervised Learning/6. The Voting Classifier.mp4 213.8 MB
  • 4. Cluster Analysis and Unsupervised Learning/1. Cluster Analysis, an overview.mp4 212.0 MB
  • 1. Introduction/1. Introduction.mp4 206.9 MB
  • 2. Regression, Prediction and Supervised Learning/2. The Traditional Simple Regression Model (II).mp4 182.0 MB
  • 1. Introduction/3. Download and installation of the Anaconda Distribution (optional).mp4 166.3 MB
  • 2. Regression, Prediction and Supervised Learning/1. Regression, Prediction, and Supervised Learning. Section Overview (I).mp4 127.7 MB
  • 2. Regression, Prediction and Supervised Learning/5. Some practical and useful modelling concepts (V).mp4 126.1 MB
  • 3. Classification and Supervised Learning/1. Classification and Supervised Learning, overview.mp4 124.0 MB
  • 2. Regression, Prediction and Supervised Learning/4. Some practical and useful modelling concepts (IV).mp4 120.2 MB
  • 1. Introduction/2. Setup of the Anaconda Cloud Notebook.mp4 113.7 MB
  • 2. Regression, Prediction and Supervised Learning/8. Multivariate Polynomial Multiple Regression models (VIII).mp4 104.5 MB
  • 2. Regression, Prediction and Supervised Learning/6.1 DiaB.csv 107.5 kB
  • 2. Regression, Prediction and Supervised Learning/7.1 DiaB.csv 97.2 kB
  • 2. Regression, Prediction and Supervised Learning/9.1 DiaB.csv 97.2 kB
  • 2. Regression, Prediction and Supervised Learning/12. Random Forest Regression (XII).srt 72.6 kB
  • 2. Regression, Prediction and Supervised Learning/10.1 insurance.csv 71.5 kB
  • 2. Regression, Prediction and Supervised Learning/12.1 insurance.csv 71.5 kB
  • 3. Classification and Supervised Learning/4. The Decision Tree Classifier.srt 64.4 kB
  • 2. Regression, Prediction and Supervised Learning/11.2 insurance.csv 61.3 kB
  • 2. Regression, Prediction and Supervised Learning/13.1 insurance.csv 61.3 kB
  • 3. Classification and Supervised Learning/4.2 insurance.csv 61.3 kB
  • 2. Regression, Prediction and Supervised Learning/10. Regression Regularization, Lasso and Ridge models (X).srt 58.6 kB
  • 2. Regression, Prediction and Supervised Learning/11. Decision Tree Regression models (XI).srt 52.9 kB
  • 2. Regression, Prediction and Supervised Learning/13. Voting Regression (XIII).srt 48.3 kB
  • 2. Regression, Prediction and Supervised Learning/9. Multivariate Polynomial Multiple Regression models (VIIII).srt 46.9 kB
  • 2. Regression, Prediction and Supervised Learning/6. Linear Multiple Regression model (VI).srt 44.8 kB
  • 3. Classification and Supervised Learning/2. Logistic Regression Classifier.srt 43.5 kB
  • 3. Classification and Supervised Learning/3. The Naive Bayes Classifier.srt 41.4 kB
  • 4. Cluster Analysis and Unsupervised Learning/3. Density-Based Spatial Clustering of Applications with Noise (DBSCAN).srt 38.9 kB
  • 3. Classification and Supervised Learning/5. The Random Forest Classifier.srt 38.2 kB
  • 2. Regression, Prediction and Supervised Learning/3. The Traditional Simple Regression Model (III).srt 33.4 kB
  • 1. Introduction/4. The Conda Package Management System (optional).srt 29.0 kB
  • 4. Cluster Analysis and Unsupervised Learning/2. K-Means Cluster Analysis, and an introduction to auto-updated K-means algorithms.srt 28.9 kB
  • 4. Cluster Analysis and Unsupervised Learning/1. Cluster Analysis, an overview.srt 26.4 kB
  • 2. Regression, Prediction and Supervised Learning/12.2 Random_Forest_regression.py 25.4 kB
  • 2. Regression, Prediction and Supervised Learning/2. The Traditional Simple Regression Model (II).srt 24.1 kB
  • 1. Introduction/1. Introduction.srt 24.1 kB
  • 2. Regression, Prediction and Supervised Learning/7. Linear Multiple Regression model (VII).srt 23.5 kB
  • 4. Cluster Analysis and Unsupervised Learning/4. Four Hierarchical Clustering algorithms.srt 23.1 kB
  • 3. Classification and Supervised Learning/6. The Voting Classifier.srt 21.1 kB
  • 1. Introduction/3. Download and installation of the Anaconda Distribution (optional).srt 21.0 kB
  • 3. Classification and Supervised Learning/1. Classification and Supervised Learning, overview.srt 20.0 kB
  • 3. Classification and Supervised Learning/5.2 Random_Forest_Classifier.py 17.0 kB
  • 2. Regression, Prediction and Supervised Learning/4. Some practical and useful modelling concepts (IV).srt 15.3 kB
  • 2. Regression, Prediction and Supervised Learning/5. Some practical and useful modelling concepts (V).srt 15.2 kB
  • 3. Classification and Supervised Learning/3.1 iris.csv 13.7 kB
  • 1. Introduction/2. Setup of the Anaconda Cloud Notebook.srt 13.0 kB
  • 2. Regression, Prediction and Supervised Learning/1. Regression, Prediction, and Supervised Learning. Section Overview (I).srt 12.6 kB
  • 2. Regression, Prediction and Supervised Learning/8. Multivariate Polynomial Multiple Regression models (VIII).srt 11.1 kB
  • 3. Classification and Supervised Learning/4.1 Decision_Tree_Classification.py 9.7 kB
  • 2. Regression, Prediction and Supervised Learning/10.2 Regularization_Ridge_Lasso_Regression.py 9.1 kB
  • 2. Regression, Prediction and Supervised Learning/13.2 Voting_Regression_Ensemble.py 8.3 kB
  • 4. Cluster Analysis and Unsupervised Learning/3.1 DBscan.py 8.3 kB
  • 3. Classification and Supervised Learning/6.1 Voting Classifier.py 6.0 kB
  • 2. Regression, Prediction and Supervised Learning/11.1 DecisionTree_regression.py 6.0 kB
  • 3. Classification and Supervised Learning/3.2 Naive_Bayes_Gaussian.py 5.8 kB
  • 2. Regression, Prediction and Supervised Learning/9.2 Mult_poly_regr.py 5.8 kB
  • 4. Cluster Analysis and Unsupervised Learning/2.2 K_means_part_2.py 5.7 kB
  • 4. Cluster Analysis and Unsupervised Learning/4.1 agglo_clustering.py 5.5 kB
  • 3. Classification and Supervised Learning/2.2 Logistic_Regression_Classifier.py 4.5 kB
  • 3. Classification and Supervised Learning/2.1 iris.csv 3.4 kB
  • 3. Classification and Supervised Learning/5.1 iris.csv 3.4 kB
  • 4. Cluster Analysis and Unsupervised Learning/2.1 K_means_part_1.py 2.7 kB
  • 2. Regression, Prediction and Supervised Learning/7.2 Linear_Multiple_Regression_Forward_Selection.py 2.4 kB
  • 2. Regression, Prediction and Supervised Learning/6.2 Multiple_Linear_Regression.py 2.3 kB
  • 2. Regression, Prediction and Supervised Learning/3.1 Regression_III.py 557 Bytes

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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！