This is an ongoing compilation of cool ML projects on github in Python. There is a lot more for me to explore! If you would like to submit a project you've made or that you have found, submit an issue with the link of the project (having something to do with AI or ML and in Python or Jupyter notebooks)

Note: An important aspect of any GitHub project is the readme, most if not all of the projects listed below have good readmes that explain the purpose of the project as well as how to run it. If you have an awesome project you want the world to look at it make sure to make an awesome readme

• Gaps: A Genetic Algorithm-Based Solver for Jigsaw Puzzles
• Rocket League Agent: Rocket League Distributed Deep Reinforcement Learning Bot
• Sandspiel: Creative cellular automata browser game
• Multi-Agent RL for Poker: Framework for Multi-Agent Deep Reinforcement Learning in Poker

• Music generation w/ CA: Music generation using Elementary Cellular Automata
• Music Source Seperation: Deep neural networks for separating singing voice from music written in TensorFlow

• Project Origin: An Artificial Life simulator for Investigating Noogenesis

• GAN Heightmaps: Heightmap generation with GANs
• Wildfire Model: Can we apply machine learning techniques to predict where wildfires are most likely to spread?
• Urban Land use Model: A land use model based on Cellular Automata and Agent-based modelling

• SDN DDoS Simulation: An attempt to detect and prevent DDoS attacks using reinforcement learning. The simulation was done using Mininet

• Emotional Dialogue: A Deep Reinforcement Learning Approach (LSTM + policy gradient) to create a chatbot that produces coherent, emotional dialogue

• Exoplanet Hunt (Help Wanted): Machine Learning apporach to detecting Exoplanets
• Galaxy Classification: Classifying galaxies using various machine learning models
• Classify Asteroid Families: Identify asteroid families using machine-learning

• Asses Odds of MBA Admission: Assessing Odds of MBA Admission. Webscrape for Data Collection and some Machine Learning samples.

• Reinforcement learning for energy systems
• Electric Vehicle Routing
• HEPAutoencoders

• Chem Reactions: Optimizing Chemical Reactions with Deep Reinforcement Learning
• Molecular Autoencoder: ML-compression of ATLAS trigger jet events using autoencoders

• Autoencoder for FMRI Volumes

• Frame level anamoly detection in videos
• Icon color: Automatic icon colorization using deep convolutional neural networks

• Link Prediction with ML: Representation learning for link prediction within social networks

• ChaosNetwork: A design for a new deep learning model architecture inspired by the human brain