Machine Learning Notebooks

Welcome to the Machine Learning Notebooks!

Prerequisites (see below)


  1. The Machine Learning landscape
  2. End-to-end Machine Learning project
  3. Classification
  4. Training Models
  5. Support Vector Machines
  6. Decision Trees
  7. Ensemble Learning and Random Forests
  8. Dimensionality Reduction
  9. Unsupervised Learning Techniques
  10. Artificial Neural Nets with Keras
  11. Training Deep Neural Networks
  12. Custom Models and Training with TensorFlow
  13. Loading and Preprocessing Data
  14. Deep Computer Vision Using Convolutional Neural Networks
  15. Processing Sequences Using RNNs and CNNs
  16. Natural Language Processing with RNNs and Attention
  17. Autoencoders, GANs, and Diffusion Models
  18. Reinforcement Learning
  19. Training and Deploying TensorFlow Models at Scale

Scientific Python tutorials

Math Tutorials

Extra Material



To understand

To run the examples