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Machine Learning.Teach by Doing(LinkedIn post)

This is reopst of LinkedIN post. Current post contains list of references and some additional detilas

I(Author) started the Machine Learning: Teach by Doing series to transfer my learnings to those who want to transition to Machine Learning.

I(Author) have recorded 37 videos in the past 6 months.

Here are the links for you to learn:

  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
  30. Introduction to Convolutional Neural Networks (CNNs.: https://lnkd.in/gpmuBm3j
  31. Filters in 1D and the Convolution Operation: https://lnkd.in/gEDaKHDU
  32. Filters in 2D, Channels and Feature Identification: https://lnkd.in/g3Gf_4ia
  33. Filtering Layers in Convolutional Neural Networks: https://lnkd.in/gUaiBkTu
  34. What is Max Pooling in Convolutional Neural Networks?: https://lnkd.in/gGRGy6wq
  35. CNN Architecture explained: https://lnkd.in/gPQvRh9i
  36. Backpropagation in Convolutional Neural Networks: https://lnkd.in/g942G6zv
  37. Build your own brain tumor classification CNN application in Python: https://lnkd.in/gQB5zRGk

Join our AI live lectures waitlist here: https://lnkd.in/gDcHZdHg