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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

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