A Beginner's Guide To Machine Learning with Unity

What if you could build a character that could learn while it played. Think about the types of gameplay you could develop where the enemies started to outsmart the player. This is what machine learning in games is all about.

In this course, we will discover the fascinating world of artificial intelligence beyond the simple stuff and examine the increasingly popular domain of machines that learn to think for themselves. In this course, Penny introduces the popular machine learning techniques of genetic algorithms and neural networks using her internationally acclaimed teaching style and knowledge from a Ph. D in game character AI and over 25 years experience working with games and computer graphics.

In addition she's written two award winning books on games AI and two others best sellers on Unity game development. Throughout the course you will follow along with hands-on workshops designed to teach you about the fundamental machine learning techniques, distilling the mathematics in a way that the topic becomes accessible to the most noob of novices. Learn how to program and work with:genetic algorithmsneural networkshuman player captured training setsreinforcement learningUnity's ML-Agent pluginTensorflowContents and OverviewThe course starts with a thorough examination of genetic algorithms that will ease you into one of the simplest machine learning techniques that is capable of extraordinary learning.

You'll develop an agent that learns to camouflage, a Flappy Bird inspired application in which the birds learn to make it through a maze and environment-sensing bots that learn to stay on a platform. Following this, you'll dive right into creating your very own neural network in C# from scratch. With this basic neural network, you will find out how to train behaviour, capture and use human player data to train an agent and teach a bot to drive.