Machine Learning A-Z

• Solutions of Machine Learning A-Z - Udemy from personal practice
• Data repository for the Machine Learning course by Kirill Eremenko and Hadelin de Ponteves.

TOC

Part 0: Welcome to the Course

Section 1. Welcome to the course!- Meet your instructors

Part 1: Data Preprocessing

Section 2. Data Preprocessing

• Machine Learning A-Z Template Folder
• Data-Preprocessing.zip

Part 2: Regression

Section 3. Welcome to Part 2!

• N/A

Section 4. Simple Linear Regression

• Simple-Linear-Regression.zip

Section 5. Multiple Linear Regression

• Step-by-step-Blueprints-For-Building-Models.pdf- Multiple-Linear-Regression.zip- Homework-Solutions.zip

Section 6. Polynomial Regression

• Polynomial-Regression.zip- Regression-Template.zip

Section 7. Support Vector Regression (SVR)

• SVR.zip

Section 8. Decision Tree Regression

• Decision-Tree-Regression.zip

Section 9. Random Forest Regression

• Random-Forest-Regression.zip

Section 10. Evaluating Regression Models Performance

• Regression-Pros-Cons.pdf- Regularization.pdf

Section 11. Regularization Methods

• TBA

Section 12. Sections Recap

• TBA

Part 3: Classification

Section 13. Welcome to Part 3!

• N/A

Section 14. Logistic Regression

• Logistic-Regression.zip- Classification-Template.zip

Section 15. K-Nearest Neighbors (K-NN)

• K-Nearest-Neighbors.zip

Section 16. Support Vector Machine (SVM)

• SVM.zip

Section 17. Kernel SVM

• Kernel-SVM.zip

Section 18. Naive Bayes

• Naive-Bayes.zip

Section 19. Decision Tree Classification

• Decision-Tree-Classification.zip

Section 20. Random Forest Classification

• Random-Forest-Classification.zip

Section 21. Evaluating Classification Models Performance

• Classification-Pros-Cons.pdf

Section 22. Part Recap

• TBA

Part 4: Clustering

Section 23. Welcome to part 4!

• N/A

Section 24. K-Means Clustering

• K-Means.zip

Section 25. Hierarchical Clustering

• Hierarchical-Clustering.zip- Clustering-Pros-Cons.pdf

Section 26. Part Recap

• TBA

Part 5: Association Rule Learning

Section 27. Welcome to part 5!

• N/A

Section 28. Apriori

• Apriori-R.zip- Apriori-Python.zip

Section 29. Eclat

• Eclat.zip

Section 30. Part Recap

• TBA

Part 6: Reinforcement Learning

Section 31. Welcome to the part 6!

• N/A

Section 32. Upper Confidence Bound (UCB)

• UCB.zip

Section 33. Thompson Sampling

• Thompson-Sampling.zip

Section 34. Part 6 Recap

• TBA

Part 7: Natural Language Processing

Section 35. Welcome to Part 7!

• N/A

Section 36: Natural Language Processing Algorithms

• Natural-Language-Processing.zip

Section 37. Part 7 Recap

• TBA

Part 8: Deep Learning

Section 38. Welcome to Part 8!

• N/A

Section 39. Artificial Neural Networks (ANN)

• Artificial-Neural-Networks.zip

Section 40. Convolutional Neural Networks (CNN)

• Convolutional-Neural-Networks.zip

Section 41. Part 8 Recap

• TBA

Part 9:Dimensionality Reduction

Section 42. Welcome to Part 9!

• N/A

Section 43. Principal Component Analysis (PCA)

• PCA.zip

Section 44. Linear Discriminant Analysis (LDA)

• LDA.zip

Section 45. Kernel PCA

• Kernel-PCA.zip

Section 46. Part 9 Recap

• TBA

Part 10: Model Selection

Section 47. Welcome to Part 10!

• N/A

Section 48: Model Selection

• Model-Selection.zip

Section 49: XGBoost

• XGBoost.zip

Reference(s)

https://www.superdatascience.com/pages/machine-learning

Special Thanks

• Kirill Eremenko
• Hadelin de Ponteves
• Udemy
• superdatascience.com