Adaptive foreground-background segmentation using Gaussian Mixture Models (GMMs)

This project explores a deep reinforcement learning technique to train an agent to play atari pong game from OpenAI Gym. OpenAI Gym is a toolkit to develop and compare reinforcement learning algorithms. The learning agent takes raw pixels from the atari emulator and predicts an action that is fed back into the emulator via OpenAI interface. The deep reinforcement learning network used in this project is Deep Q Network (DQN), it took over 10 million episodes to perfectly play and win the game.

Personal capstone project for Udacity's Machine Learning Nanodegree (MLND).

Capstone Project for Udacity Machine Learning Nanodegree

Capstone project for the Udacity MLND on the prediction of customer acquisition based on regional demographics and customer attributes. With the help of supervised and unsupervised learning techniques.