Supporting Code for Machine Learning Course

This is a project with supporting code for Machine Learning course based on MIT 6.036 course and Microsoft ML for beginners course.

Courses

• MIT 6.036 ML Course: https://openlearninglibrary.mit.edu/courses/course-v1:MITx+6.036+1T2019/course/
• Microsoft ML for Beginners Course: https://github.com/microsoft/ML-For-Beginners

Video Lessons

1. Introduction to Machine Learning Teach by Doing: https://lnkd.in/gqN2PMX5
2. What is Machine Learning? History of Machine Learning: https://lnkd.in/gvpNSAKh
3. Types of ML Models: https://lnkd.in/gSy2mChM
4. 6 steps of any ML project: https://lnkd.in/ggCGchPQ
5. Install Python and VSCode and run your first code: https://lnkd.in/gyic7J7b
6. Linear Classifiers Part 1: https://lnkd.in/gYdfD97D
7. Linear Classifiers Part 2: https://lnkd.in/gac_z-G8
8. Jupyter Notebook, Numpy and Scikit-Learn: https://lnkd.in/gWRaC_tB
9. Running the Random Linear Classifier Algorithm in Python: https://lnkd.in/g5HacbFC
10. The oldest ML model - Perceptron: https://lnkd.in/gpce6uFt
11. Coding the Perceptron: https://lnkd.in/gmz-XjNK
12. Perceptron Convergence Theorem: https://lnkd.in/gmz-XjNK
13. Magic of features in Machine Learning: https://lnkd.in/gCeDRb3g
14. One hot encoding: https://lnkd.in/g3WfRQGQ
15. Logistic Regression Part 1: https://lnkd.in/gTgZAAZn
16. Cross Entropy Loss: https://lnkd.in/g3Ywg_2p
17. How gradient descent works: https://lnkd.in/gKBAsazF
18. Logistic Regression from scratch in Python: https://lnkd.in/g8iZh27P
19. Introduction to Regularization: https://lnkd.in/gjM9pVw2
20. Implementing Regularization in Python: https://lnkd.in/gRnSK4v4
21. Linear Regression Introduction: https://lnkd.in/gPYtSPJ9
22. Ordinary Least Squares step by step implementation: https://lnkd.in/gnWQdgNy
23. Ridge regression fundamentals and intuition: https://lnkd.in/gE5M-CSM
24. Regression recap for interviews: https://lnkd.in/gNBWzzWv
25. Neural network architecture in 30 minutes: https://lnkd.in/g7qSrkxG
26. Backpropagation intuition: https://lnkd.in/gAmBARHm
27. Neural network activation functions: https://lnkd.in/gqrC3zDP
28. Momentum in gradient descent: https://lnkd.in/g3M4qhbP
29. Hands on neural network training in Python: https://lnkd.in/gz-fTBxs