Greetings
Welcome to the data repository for the Machine Learning course by Kirill Eremenko and Hadelin de Ponteves. The datasets and other supplementary materials are below. Enjoy!
Part 0: Welcome to the Course
Section 1. Welcome to the course!
Part 1: Data Preprocessing
Section 2. Welcome to Part 1!
Section 3. Data Preprocessing in Python
Section 4. Data Preprocessing in R
Part 2: Regression
Section 5. Welcome to Part 2!
Section 6. Simple Linear Regression
Section 7. Multiple Linear Regression
Section 8. Polynomial Regression
Section 9. Support Vector Regression (SVR)
Section 10. Decision Tree Regression
Section 11. Random Forest Regression
Section 12. Evaluating Regression Models Performance
Section 13. Regularization Methods
Section 14. Sections Recap
Part 3: Classification
Section 15. Welcome to Part 3!
Section 16. Logistic Regression
Section 17. K-Nearest Neighbors (K-NN)
Section 18. Support Vector Machine (SVM)
Section 19. Kernel SVM
Section 20. Naive Bayes
Section 21. Decision Tree Classification
Section 22. Random Forest Classification
Section 23. Evaluating Classification Models Performance
Part 4: Clustering
Section 24. Welcome to part 4!
Section 25. K-Means Clustering
Section 26. Hierarchical Clustering
Part 5: Association Rule Learning
Section 27. Welcome to part 5!
Section 28. Apriori
Section 29. Eclat
Part 6: Reinforcement Learning
Section 30. Welcome to the part 6!
Section 31. Upper Confidence Bound (UCB)
Section 32. Thompson Sampling
Part 7: Natural Language Processing
Section 33. Natural Language Processing Algorithms
Part 8: Deep Learning
Section 34. Welcome to Part 8!
Section 35. Artificial Neural Networks (ANN)
Section 36. Convolutional Neural Networks (CNN)
Part 9: Dimensionality Reduction
Section 37. Welcome to Part 9!
Section 38. Principal Component Analysis (PCA)
Section 39. Linear Discriminant Analysis (LDA)
Section 40. Kernel PCA
Part 10: Model Selection
Section 41. Welcome to Part 10!
Section 42: Model Selection
Section 43: XGBoost
