In this course you will be introduced to the basics of Machine Learning through game design in Unity and Python. First you will be introduced to the very basics of ML and python, you will learn such simple ML concepts such as supervised learning, regression and gradient descent. After that you will get acquainted with reinforcement learning and will try to understand and apply a combination of Reinforcement Learning, Deep Neural Networks and other algorithms in the Unity environment to help your agent accomplish complex and dynamic tasks. Lastly you will learn how to build dynamic full featured RL environments and agents for you to train later in your games. You will not only be able to create your own RL algorithms from scratch (such as Q-learning, SARSA and PPO), but also customize them to fit the needs of your environment and train your agents in Unity. You will gain experience with widely used tools and libraries in the industry of ML, such as: Unity3D, Pytorch, mlagents-learn, scikit-learn and more. We hope that this course will help you to better understand and prepare for your journey of developing truly intelligent agents and characters in your own games. Let's get started!