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

Creation Time	Aug. 12, 2025, 5:50 a.m.
Last Access Time	Aug. 22, 2025, 9:29 a.m.
File Size	7.1 GB
Keywords	and Methods Science - Udemy 2024 2025 7 Data Techniques
Total Requests	24
Total Files	82
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3. Classification and Supervised Learning/4. The Decision Tree Classifier.mp4 549.1 MB
2. Regression, Prediction and Supervised Learning/10. Regression Regularization, Lasso and Ridge models (X).mp4 520.4 MB
2. Regression, Prediction and Supervised Learning/11. Decision Tree Regression models (XI).mp4 476.1 MB
2. Regression, Prediction and Supervised Learning/12. Random Forest Regression (XII).mp4 410.6 MB
2. Regression, Prediction and Supervised Learning/9. Multivariate Polynomial Multiple Regression models (VIIII).mp4 408.8 MB
3. Classification and Supervised Learning/2. Logistic Regression Classifier.mp4 379.6 MB
2. Regression, Prediction and Supervised Learning/6. Linear Multiple Regression model (VI).mp4 338.3 MB
4. Cluster Analysis and Unsupervised Learning/3. Density-Based Spatial Clustering of Applications with Noise (DBSCAN).mp4 334.4 MB
3. Classification and Supervised Learning/5. The Random Forest Classifier.mp4 331.5 MB
3. Classification and Supervised Learning/3. The Naive Bayes Classifier.mp4 328.3 MB
2. Regression, Prediction and Supervised Learning/3. The Traditional Simple Regression Model (III).mp4 317.5 MB
1. Introduction/4. The Conda Package Management System (optional).mp4 268.3 MB
4. Cluster Analysis and Unsupervised Learning/2. K-Means Cluster Analysis, and an introduction to auto-updated K-means algorithms.mp4 253.7 MB
2. Regression, Prediction and Supervised Learning/13. Voting Regression (XIII).mp4 244.9 MB
2. Regression, Prediction and Supervised Learning/7. Linear Multiple Regression model (VII).mp4 233.0 MB
4. Cluster Analysis and Unsupervised Learning/4. Four Hierarchical Clustering algorithms.mp4 210.6 MB
3. Classification and Supervised Learning/6. The Voting Classifier.mp4 203.9 MB
4. Cluster Analysis and Unsupervised Learning/1. Cluster Analysis, an overview.mp4 202.2 MB
1. Introduction/1. Introduction.mp4 197.3 MB
2. Regression, Prediction and Supervised Learning/2. The Traditional Simple Regression Model (II).mp4 173.6 MB
1. Introduction/3. Download and installation of the Anaconda Distribution (optional).mp4 158.6 MB
2. Regression, Prediction and Supervised Learning/1. Regression, Prediction, and Supervised Learning. Section Overview (I).mp4 121.8 MB
2. Regression, Prediction and Supervised Learning/5. Some practical and useful modelling concepts (V).mp4 120.2 MB
3. Classification and Supervised Learning/1. Classification and Supervised Learning, overview.mp4 118.3 MB
2. Regression, Prediction and Supervised Learning/4. Some practical and useful modelling concepts (IV).mp4 114.7 MB
1. Introduction/2. Setup of the Anaconda Cloud Notebook.mp4 108.4 MB
2. Regression, Prediction and Supervised Learning/8. Multivariate Polynomial Multiple Regression models (VIII).mp4 99.7 MB
2. Regression, Prediction and Supervised Learning/6.1 DiaB.csv 105.0 KB
2. Regression, Prediction and Supervised Learning/7.1 DiaB.csv 95.0 KB
2. Regression, Prediction and Supervised Learning/9.1 DiaB.csv 95.0 KB
2. Regression, Prediction and Supervised Learning/12. Random Forest Regression (XII).srt 70.9 KB
2. Regression, Prediction and Supervised Learning/10.1 insurance.csv 69.8 KB
2. Regression, Prediction and Supervised Learning/12.1 insurance.csv 69.8 KB
3. Classification and Supervised Learning/4. The Decision Tree Classifier.srt 62.9 KB
2. Regression, Prediction and Supervised Learning/11.2 insurance.csv 59.8 KB
2. Regression, Prediction and Supervised Learning/13.1 insurance.csv 59.8 KB
3. Classification and Supervised Learning/4.2 insurance.csv 59.8 KB
2. Regression, Prediction and Supervised Learning/10. Regression Regularization, Lasso and Ridge models (X).srt 57.2 KB
2. Regression, Prediction and Supervised Learning/11. Decision Tree Regression models (XI).srt 51.7 KB
2. Regression, Prediction and Supervised Learning/13. Voting Regression (XIII).srt 47.1 KB
2. Regression, Prediction and Supervised Learning/9. Multivariate Polynomial Multiple Regression models (VIIII).srt 45.8 KB
2. Regression, Prediction and Supervised Learning/6. Linear Multiple Regression model (VI).srt 43.8 KB
3. Classification and Supervised Learning/2. Logistic Regression Classifier.srt 42.5 KB
3. Classification and Supervised Learning/3. The Naive Bayes Classifier.srt 40.4 KB
4. Cluster Analysis and Unsupervised Learning/3. Density-Based Spatial Clustering of Applications with Noise (DBSCAN).srt 38.0 KB
3. Classification and Supervised Learning/5. The Random Forest Classifier.srt 37.3 KB
2. Regression, Prediction and Supervised Learning/3. The Traditional Simple Regression Model (III).srt 32.6 KB
1. Introduction/4. The Conda Package Management System (optional).srt 28.3 KB
4. Cluster Analysis and Unsupervised Learning/2. K-Means Cluster Analysis, and an introduction to auto-updated K-means algorithms.srt 28.2 KB
4. Cluster Analysis and Unsupervised Learning/1. Cluster Analysis, an overview.srt 25.8 KB
2. Regression, Prediction and Supervised Learning/12.2 Random_Forest_regression.py 24.8 KB
2. Regression, Prediction and Supervised Learning/2. The Traditional Simple Regression Model (II).srt 23.6 KB
1. Introduction/1. Introduction.srt 23.5 KB
2. Regression, Prediction and Supervised Learning/7. Linear Multiple Regression model (VII).srt 23.0 KB
4. Cluster Analysis and Unsupervised Learning/4. Four Hierarchical Clustering algorithms.srt 22.6 KB
3. Classification and Supervised Learning/6. The Voting Classifier.srt 20.6 KB
1. Introduction/3. Download and installation of the Anaconda Distribution (optional).srt 20.6 KB
3. Classification and Supervised Learning/1. Classification and Supervised Learning, overview.srt 19.6 KB
3. Classification and Supervised Learning/5.2 Random_Forest_Classifier.py 16.6 KB
2. Regression, Prediction and Supervised Learning/4. Some practical and useful modelling concepts (IV).srt 15.0 KB
2. Regression, Prediction and Supervised Learning/5. Some practical and useful modelling concepts (V).srt 14.8 KB
3. Classification and Supervised Learning/3.1 iris.csv 13.3 KB
1. Introduction/2. Setup of the Anaconda Cloud Notebook.srt 12.7 KB
2. Regression, Prediction and Supervised Learning/1. Regression, Prediction, and Supervised Learning. Section Overview (I).srt 12.3 KB
2. Regression, Prediction and Supervised Learning/8. Multivariate Polynomial Multiple Regression models (VIII).srt 10.8 KB
3. Classification and Supervised Learning/4.1 Decision_Tree_Classification.py 9.5 KB
2. Regression, Prediction and Supervised Learning/10.2 Regularization_Ridge_Lasso_Regression.py 8.8 KB
2. Regression, Prediction and Supervised Learning/13.2 Voting_Regression_Ensemble.py 8.1 KB
4. Cluster Analysis and Unsupervised Learning/3.1 DBscan.py 8.1 KB
3. Classification and Supervised Learning/6.1 Voting Classifier.py 5.8 KB
2. Regression, Prediction and Supervised Learning/11.1 DecisionTree_regression.py 5.8 KB
3. Classification and Supervised Learning/3.2 Naive_Bayes_Gaussian.py 5.6 KB
2. Regression, Prediction and Supervised Learning/9.2 Mult_poly_regr.py 5.6 KB
4. Cluster Analysis and Unsupervised Learning/2.2 K_means_part_2.py 5.6 KB
4. Cluster Analysis and Unsupervised Learning/4.1 agglo_clustering.py 5.3 KB
3. Classification and Supervised Learning/2.2 Logistic_Regression_Classifier.py 4.4 KB
3. Classification and Supervised Learning/2.1 iris.csv 3.3 KB
3. Classification and Supervised Learning/5.1 iris.csv 3.3 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 2.3 KB
2. Regression, Prediction and Supervised Learning/6.2 Multiple_Linear_Regression.py 2.2 KB
2. Regression, Prediction and Supervised Learning/3.1 Regression_III.py 557 bytes

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

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