Machine Learning A-Z™: Download Practice Datasets - SuperDataScience - Big Data | Analytics Careers | Mentors | Success

Machine Learning A-Z™: Download Practice Datasets

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. Data Preprocessing

 


Part 2. Regression

Section 3. Welcome to Part 2!

  • N/A

Section 4. Simple Linear Regression

Section 5. Multiple Linear Regression

Section 6. Polynomial Regression

Section 7. Support Vector Regression (SVR)

Section 8. Decision Tree Regression

Section 9. Random Forest Regression

Section 10. Evaluating Regression Models Performance

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

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

Section 16. Support Vector Machine (SVM)

Section 17. Kernel SVM

Section 18. Naive Bayes

Section 19. Decision Tree Classification

Section 20. Random Forest Classification

Section 21. Evaluating Classification Models Performance

Section 22. Part Recap

  • TBA

Part 4. Clustering

Section 23. Welcome to part 4!

  • N/A

Section 24. K-Means Clustering

Section 25. Hierarchical Clustering

Section 26. Part Recap

  • TBA

Part 5. Association Rule Learning

Section 27. Welcome to part 5!

  • N/A

Section 28. Apriori

Section 29. Eclat

Section 30. Part Recap

  • TBA

Part 6. Reinforcement Learning

Section 31. Welcome to the part 6!

  • N/A

Section 32. Upper Confidence Bound (UCB)

Section 33. Thompson Sampling

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

Section 37. Part 7 Recap

  • TBA

Part 8. Deep Learning

Section 38. Welcome to Part 8!

  • N/A

Section 39. Artificial Neural Networks (ANN)

Section 40. Convolutional Neural Networks (CNN)

Section 41. Part 8 Recap

  • TBA

Part 9. Dimensionality Reduction

Section 42. Welcome to Part 9!

  • N/A

Section 43. Principal Component Analysis (PCA)

Section 44. Linear Discriminant Analysis (LDA)

Section 45. Kernel PCA

Section 46. Part 9 Recap

  • TBA

Part 10. Model Selection 

Section 47. Welcome to Part 10!

  • N/A

Section 48: Model Selection 

Section 49: XGBoost

Kirill Eremenko
Kirill Eremenko

I’m a Data Scientist and Entrepreneur. I also teach Data Science Online and host the SDS podcast where I interview some of the most inspiring Data Scientists from all around the world. I am passionate about bringing Data Science and Analytics to the world!

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