Tutorialsplanet NET Udemy Machine Learning Hands On Python In Data Science |
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[Tutorialsplanet.NET] Udemy - Machine Learning A-Z™ Hands-On Python & R In Data Science |
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Total Size |
12.0 GB |
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Total Files |
615 |
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Hash |
DE282F69CE08FFF6329CCB45D0FFB900DE2856FF |
/1. Welcome to the course!/ |
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10. Presentation of the ML A-Z folder, Colaboratory, Jupyter Notebook and Spyder.mp4 |
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10. Presentation of the ML A-Z folder, Colaboratory, Jupyter Notebook and Spyder.srt |
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/10. Decision Tree Regression/ |
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2. Make sure you have your Machine Learning A-Z folder ready.html |
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/11. Random Forest Regression/ |
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/12. Evaluating Regression Models Performance/ |
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/13. Regression Model Selection in Python/ |
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3. THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION!.mp4 |
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3. THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION!.srt |
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/14. Regression Model Selection in R/ |
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1. Evaluating Regression Models Performance - Homework's Final Part.mp4 |
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1. Evaluating Regression Models Performance - Homework's Final Part.srt |
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/15. -------------------- Part 3 Classification --------------------/ |
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/16. Logistic Regression/ |
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17. Machine Learning Regression and Classification BONUS.html |
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/17. K-Nearest Neighbors (K-NN)/ |
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/18. Support Vector Machine (SVM)/ |
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/19. Kernel SVM/ |
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/2. -------------------- Part 1 Data Preprocessing --------------------/ |
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/20. Naive Bayes/ |
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/21. Decision Tree Classification/ |
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/22. Random Forest Classification/ |
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/23. Classification Model Selection in Python/ |
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2. THE ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION!.mp4 |
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2. THE ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION!.srt |
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/24. Evaluating Classification Models Performance/ |
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/25. -------------------- Part 4 Clustering --------------------/ |
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/26. K-Means Clustering/ |
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/27. Hierarchical Clustering/ |
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/28. -------------------- Part 5 Association Rule Learning --------------------/ |
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/29. Apriori/ |
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/3. Data Preprocessing in Python/ |
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5. For Python learners, summary of Object-oriented programming classes & objects.html |
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8. Splitting the dataset into the Training set and Test set.mp4 |
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8. Splitting the dataset into the Training set and Test set.srt |
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/30. Eclat/ |
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/31. -------------------- Part 6 Reinforcement Learning --------------------/ |
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/32. Upper Confidence Bound (UCB)/ |
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/33. Thompson Sampling/ |
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/35. -------------------- Part 8 Deep Learning --------------------/ |
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/36. Artificial Neural Networks/ |
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/37. Convolutional Neural Networks/ |
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/38. -------------------- Part 9 Dimensionality Reduction --------------------/ |
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/39. Principal Component Analysis (PCA)/ |
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/4. Data Preprocessing in R/ |
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8. Splitting the dataset into the Training set and Test set.mp4 |
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8. Splitting the dataset into the Training set and Test set.srt |
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/40. Linear Discriminant Analysis (LDA)/ |
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/41. Kernel PCA/ |
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/42/ |
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/43. Model Selection/ |
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/44. XGBoost/ |
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/45. Bonus Lectures/ |
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/5. -------------------- Part 2 Regression --------------------/ |
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/6. Simple Linear Regression/ |
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/7. Multiple Linear Regression/ |
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18. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp4 |
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18. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.srt |
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19. Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4 |
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19. Multiple Linear Regression in R - Backward Elimination - Homework Solution.srt |
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/8. Polynomial Regression/ |
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/9. Support Vector Regression (SVR)/ |
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