mlatoz

Machine Learning A-Z: AI, Python & R + ChatGPT Bonus [2023]

This repository contains the code for the algorithms implemented in the Udemy course "Machine Learning A-Z: AI, Python & R" by Hadelin de Ponteves and Kirill Eremenko.

Certificate of Completion

Course Overview

The course covers a wide range of machine learning algorithms and techniques. It provides hands-on experience with implementing these algorithms in both Python and R.

Here is the course overview:-

  1. Section 01 - Welcome
  2. Section 02 - Part 01 - Data Preprocessing
  3. Section 03 - Data Preprocessing in Python
  4. Section 04 - Data Preprocessing in R
  5. Section 05 - Part 02 - Regression
  6. Section 06 - Simple Linear Regression
  7. Section 07 - Multiple Linear Regression
  8. Section 08 - Polynomial Regression
  9. Section 09 - Support Vector Regression (SVR)
  10. Section 10 - Decision Tree Regression
  11. Section 11 - Random Forest Regression
  12. Section 12 - Evaluating Regression Models Performance
  13. Section 13 - Regression Model Selection in Python
  14. Section 14 - Regression Model Selection in R
  15. Section 15 - Part 03 - Classification
  16. Section 16 - Logistic Regression
  17. Section 17 - K-Nearest Neighbors (K-NN)
  18. Section 18 - Support Vector Machine (SVM)
  19. Section 19 - Kernel SVM
  20. Section 20 - Naive Bayes
  21. Section 21 - Decision Tree Classification
  22. Section 22 - Random Forest Classification
  23. Section 23 - Classification Model Selection in Python
  24. Section 24 - Evaluating Classification Models Performance
  25. Section 25 - Part 04 - Clustering
  26. Section 26 - K-Means Clustering
  27. Section 27 - Hierarchical Clustering
  28. Section 28 - Part 05 - Association Rule Learning
  29. Section 29 - Apriori
  30. Section 30 - Eclat
  31. Section 31 - Part 06 - Reinforcement Learning
  32. Section 32 - Upper Confidence Bound (UCB)
  33. Section 33 - Thompson Sampling
  34. Section 34 - Part 07 - Natural Language Processing (NLP)
  35. Section 35 - Part 08 - Deep Learning
  36. Section 36 - Artificial Neural Networks (ANNs)
  37. Section 37 - Convolutional Neural Networks (CNNs)
  38. Section 38 - Recurrent Neural Networks (RNNs)
  39. Section 39 - Self Organizing Maps (SOMs)
  40. Section 40 - Boltzmann Machines
  41. Section 41 - AutoEncoders
  42. Section 42 - Part 09 - Dimensionality Reduction
  43. Section 43 - Principal Component Analysis (PCA)
  44. Section 44 - Linear Discriminant Analysis (LDA)
  45. Section 45 - Kernel PCA
  46. Section 46 - Part 10 - Model Selection & Boosting
  47. Section 47 - Model Selection
  48. Section 48 - XGBoost
  49. Section 49 - Annex - Logistic Regression (Long Explanation)
  50. Section 50 - Code Templates

Usage

To use the code in this repository, clone the repository and navigate to the specific section you're interested in. Each section contains a separate README file with instructions on how to run the code.

Contributing

Contributions are welcome! Please read the contributing guidelines before making any changes.

License

This project is licensed under the MIT License. See the LICENSE file for details.

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