Skip to content

This is the repository for the course ML at Asian Institute of Technology. Covers machine learning and deep learning from scratch using Python.

Notifications You must be signed in to change notification settings

chaklam-silpasuwanchai/Python-for-Machine-Learning

Repository files navigation

Machine Learning

This is the repository for the course Machine Learning at Asian Institute of Technology.

Corresponding YT: https://www.youtube.com/watch?v=KnP5n8TSJc4&list=PLqL-7eLmqd9ViCe07M6WiCVyaWJRc_plF

Prerequisites

  • Visit our "Prerequisites" folder to review the materials, before attempting our ML course

Technology stack

  • NumPy, Pandas, Matplotlib, Sklearn, PyTorch - for machine and deep learning
  • MLFlow - for experimenting
  • FastAPI - for exposing the models
  • Anything for frontend, e.g., Vue, ReAct, Angular, Jinja, Hugo, etc.
  • Anything for backend, e.g., Django, Flask
  • Docker for containerization, and Traefik for reverse proxy

Outline

The course is structured into 5 big components:

0. Case Study

  • Regression
  • Classification

1. Supervised Learning

Regression

  • Gradient Descent
  • Stochastic and Mini-batch
  • Regularization

Classification

  • Logistic Regression
  • Naive Bayes
  • K-Nearest Neighbors
  • Support Vector Machines
  • Decision Trees
  • Random Forest
  • AdaBoost
  • Gradient Boosting

2. Unsupervised Learning

  • K-mean clustering
  • Gaussian mixture
  • Principle component analysis

3. Deep Learning

  • Feedforward Neural Netork
  • Convolutional Neural Network
  • Recurrent Neural Network

4. Reinforcement Learning

  • PPO

References:

About

This is the repository for the course ML at Asian Institute of Technology. Covers machine learning and deep learning from scratch using Python.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages