Machine Learning Notebook

Author

Nguyen Cao Duc Huy

Published

January 2, 2024

Welcome

Hello everyone, my name is Nguyen Cao Duc Huy. I am studying Master of Data Science at HCMUS.

This notebook covers some Machine Learning algorithms and practical exercises, has been written while I understanding ML concepts. Hopefully it will help you systemize knowledge and be able to work in this interesting and fast-evolving field.

Contents of this notebook are as follow:

  1. The Machine Learning Landscape
  2. Machine Learning Project Workflow
  3. Regression algorithms
  4. Gradient Descent
  5. Hands-on Regression
  6. Classification algorithms
  7. Hands-on Classification
  8. Support Vector Machine
  9. Decision Tree
  10. Dimensionality Reduction
  11. Ensemble Learning
  12. Unsupervised Learning

In Progress:

  • Fundamental Neural Networks (NNs)
  • Loading and Preprocessing Data for NNs
  • Training Deep NNs
  • Customize NNs
  • Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
  • Computer Vision using NNs
  • Natural Language Processing (NLP) using NNs
  • Autoencoders, GANs, and Diffusion Models
  • Reinforcement Learning
  • Training and Deploying NNs models at Scale

References

  1. Aurélien Géron (2023). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (3rd ed.). O’Reilly.
  2. Jeremy Howard & Sylvain Gugger. Deep Learning for Coders with fastai & PyTorch. O’Reilly.