Download Udemy - Supervised Machine Learning Explained - The Top 5 Models Torrent

Torrent Contents Size: 3.4 GB

Udemy - Supervised Machine Learning Explained - The Top 5 Models

Bonus Resources.txt

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102.4 B

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1 - Course Introduction

1 - Welcome! (Description).html

HTML

1.6 KB

1 - Welcome!.mp4

MP4

123.2 MB

2 - Setup & Resources (Description).html

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1.4 KB

2 - Setup & Resources.mp4

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159.8 MB

2 - What It Means For a Machine to Learn

3 - Linear Regression Learning From Error

1 - Foundations of Supervised Learning.html

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24.7 KB

10 - How to Linear Regression and Evaluation.html

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12.9 KB

11 - Overfitting and Underfitting (Description).html

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2.3 KB

11 - Overfitting and Underfitting.mp4

MP4

290.5 MB

4 - Logistic Regression Probabilities and Decisions

12 - Logistic Regression Predicting Classes (Description).html

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2.2 KB

12 - Logistic Regression Predicting Classes.mp4

MP4

230.6 MB

12 - LogisticRegressionExemplar.ipynb.bin

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26.4 KB

13 - Probabilities and Decision Thresholds (Description).html

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2.1 KB

13 - Probabilities and Decision Thresholds.mp4

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75.5 MB

14 - Confusion Matrices and Classification Metrics (Description).html

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2.3 KB

14 - Confusion Matrices and Classification Metrics.mp4

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171.8 MB

15 - How to Logistic Regression and Evaluation.html

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13.4 KB

2 - Classification and Decision-Making.html

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24.7 KB

5 - k-Nearest Neighbors Learning By Similarity

16 - k-Nearest Neighbors Distance-Based Learning (Description).html

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2 KB

16 - k-Nearest Neighbors Distance-Based Learning.mp4

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255.2 MB

16 - kNearestNeighborsExemplar.ipynb.bin

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14.8 KB

17 - How to Choose k.html

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10.6 KB

18 - Feature Scaling and Why It Matters (Description).html

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2.1 KB

18 - Feature Scaling and Why It Matters.mp4

MP4

141.7 MB

19 - How to KNN and Feature Scaling.html

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13.2 KB

3 - Similarity-Based Learning.html

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25.9 KB

6 - Decision Trees Learning Rules

20 - Decision Trees Learning Rules (Description).html

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1.8 KB

20 - Decision Trees Learning Rules.mp4

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215.4 MB

20 - DecisionTreeExemplar.ipynb.bin

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9.9 KB

21 - Tree Depth and Model Complexity (Description).html

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1.6 KB

21 - Tree Depth and Model Complexity.mp4

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178.4 MB

22 - How to Decision Trees.html

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13.5 KB

23 - Cross-Validation Testing Model Stability (Description).html

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2 KB

23 - Cross-Validation Testing Model Stability.mp4

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129.8 MB

24 - How to Cross-Validation.html

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9.1 KB

4 - Decision Trees and Model Complexity.html

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26.3 KB

7 - Random Forests Learning With Many Models

25 - Random Forests Learning With Many Models (Description).html

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1.9 KB

25 - Random Forests Learning With Many Models.mp4

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272.4 MB

25 - RandomForestExemplar.ipynb.bin

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9.9 KB

26 - How to Random Forests.html

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13.6 KB

27 - Bias Vs. Variance (Description).html

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2 KB

27 - Bias Vs. Variance.mp4

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190.5 MB

5 - Bias, Variance, and Ensembles.html

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26 KB

8 - Course Conclusion

	28 - Congratulations! And Next Steps.mp4	MP4	148.2 MB
	6 - Final Assessment.html	HTML	47.1 KB

8 - Linear Regression Predicting Numbers (Description).html

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2.3 KB

8 - Linear Regression Predicting Numbers.mp4

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202.8 MB

8 - LinearRegressionExemplar.ipynb.bin

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5.4 KB

9 - Loss Functions Measuring Error (Description).html

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2.4 KB

9 - Loss Functions Measuring Error.mp4

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254.2 MB

4 - What Learning Means in Machine Learning (Description).html

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2.6 KB

4 - What Learning Means in Machine Learning.mp4

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177.1 MB

5 - Datasets Features, Targets, and Rows (Description).html

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2.7 KB

5 - Datasets Features, Targets, and Rows.mp4

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138.7 MB

5 - episode_1_2_used_car_dataset.xlsx

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5.5 KB

6 - Train vs. Test Why We Split Data (Description).html

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2.4 KB

6 - Train vs. Test Why We Split Data.mp4

MP4

140.2 MB

7 - How To Train Test Split.html

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12.9 KB

3 - Exploring Downloadable Notebooks.html

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8.6 KB

Description

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Udemy - Supervised Machine Learning Explained - The Top 5 Models

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