A Beginner Guide To Machine Learning with Unity

A Beginners Guide To Machine Learning with Unity, available at $94.99, has an average rating of 4.54, with 78 lectures, 1 quizzes, based on 2041 reviews, and has 24406 subscribers.

You will learn about Build a genetic algorithm from scratch in C#. Build a neural network from scratch in C#. Setup and explore the Unity ML-Agents plugin. Setup and use Tensorflow to train game characters. Apply newfound knowledge of machine learning to integrate contemporary research ideas in the field into their own projects. Distill the mathematics and statistic behind machine learning to working program code. Use a Proximal Policy Optimisation to train a neural network. This course is ideal for individuals who are Anyone wanting to learn about the potential of machine learning in games. or Anyone wanting a deeper understanding of the algorithms and theories underlying Unitys ML-Agents. or Anyone wanting to know how to setup and work with ML-Agents. It is particularly useful for Anyone wanting to learn about the potential of machine learning in games. or Anyone wanting a deeper understanding of the algorithms and theories underlying Unitys ML-Agents. or Anyone wanting to know how to setup and work with ML-Agents.

Enroll now: A Beginners Guide To Machine Learning with Unity

Summary

Title: A Beginners Guide To Machine Learning with Unity

Price: $94.99

Average Rating: 4.54

Number of Lectures: 78

Number of Quizzes: 1

Number of Published Lectures: 76

Number of Published Quizzes: 1

Number of Curriculum Items: 79

Number of Published Curriculum Objects: 77

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Build a genetic algorithm from scratch in C#.

Build a neural network from scratch in C#.

Setup and explore the Unity ML-Agents plugin.

Setup and use Tensorflow to train game characters.

Apply newfound knowledge of machine learning to integrate contemporary research ideas in the field into their own projects.

Distill the mathematics and statistic behind machine learning to working program code.

Use a Proximal Policy Optimisation to train a neural network.

Who Should Attend

Anyone wanting to learn about the potential of machine learning in games.

Anyone wanting a deeper understanding of the algorithms and theories underlying Unitys ML-Agents.

Anyone wanting to know how to setup and work with ML-Agents.

Target Audiences

Anyone wanting to learn about the potential of machine learning in games.

Anyone wanting a deeper understanding of the algorithms and theories underlying Unitys ML-Agents.

Anyone wanting to know how to setup and work with ML-Agents.

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 algorithms

neural networks

human player captured training sets

reinforcement learning

Unity’s ML-Agent plugin

Tensorflow

Contents and Overview

The 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.  In the same section you’ll have the Q-learning algorithm explained, before integrating it into your own applications.

By this stage, you’ll feel confident with the terminology and techniques used throughout the deep learning community and be ready to tackle Unity’s experimental ML-Agents. Together with Tensorflow, you’ll be throwing agents in the deep-end and reinforcing their knowledge to stay alive in a variety of game environment scenarios.

By the end of the course, you’ll have a well-equipped toolset of basic and solid machine learning algorithms and applications, that will see you able to decipher the latest research publications and integrate the latest developments into your work, while keeping abreast of Unity’s ML-Agents as they evolve from experimental to production release.

What students are saying about this course:

Absolutely the best beginner to Advanced course for Neural Networks/ Machine Learning if you are a game developer that uses C# and Unity. BAR NONE x Infinity.

A perfect course with great math examples and demonstration of the TensorFlow power inside Unity. After this course, you will get the strong basic background in the Machine Learning.

The instructor is very engaging and knowledgeable. I started learning from the first lesson and it never stopped. If you are interested in Machine Learning , take this course.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: What is Learning?

Lecture 3: How to Study This Course

Lecture 4: FAQs

Lecture 5: Machine Learning 101

Chapter 2: Genetic Algorithms

Lecture 1: DNA Inspired Data Structures

Lecture 2: Camouflage Training with Genetic Algorithms Part 1

Lecture 3: Camouflage Training with Genetic Algorithms Part 2

Lecture 4: Camouflage Challenge

Lecture 5: Coding Movement with Genes Part 1

Lecture 6: Coding Movement with Genes Part 2

Lecture 7: Distance Challenge

Lecture 8: Note: Unity Versions Might Mess Up Package Imports

Lecture 9: Moving GAs with Senses Part 1

Lecture 10: Moving GAs with Senses Part 2

Lecture 11: Moving GAs with Senses Part 3

Lecture 12: Maze Walking Challenge

Lecture 13: Maze Walking Challenge Solution Part 2

Lecture 14: Not So Flappy Birds Part 1

Lecture 15: Not So Flappy Birds Part 2

Lecture 16: Extra Readings

Chapter 3: Perceptrons: The making of a Neural Network

Lecture 1: The Perceptron

Lecture 2: Challenge

Lecture 3: Programming and Training a Perceptron

Lecture 4: Exercise 1

Lecture 5: Exercise 2

Lecture 6: Perceptron Classification

Lecture 7: Perceptron Learning from Experience

Lecture 8: Saving & Loading Perceptron Values

Chapter 4: Artificial Neural Networks

Lecture 1: Introduction to Neural Networks

Lecture 2: Programming An Artificial Neural Network Part 1

Lecture 3: Programming An Artificial Neural Network Part 2

Lecture 4: Programming An Artificial Neural Network Part 3

Lecture 5: ANN FAQs

Lecture 6: Working with Activation Functions

Lecture 7: Challenge

Lecture 8: Extra Readings

Chapter 5: Neural Networks in Practice

Lecture 1: Developing a Neural Network that Plays Pong Part 1

Lecture 2: Developing a Neural Network that Plays Pong Part 2

Lecture 3: Developing a Neural Network that Plays Pong Part 3

Lecture 4: Challenge

Lecture 5: Gathering Training Data from the Player Part 1

Lecture 6: Gathering Training Data from the Player Part 2

Lecture 7: Training with Player Data Part 1

Lecture 8: A Note to the Astute

Lecture 9: Training with Player Data Part 2

Lecture 10: Training with Player Data Part 3

Chapter 6: Reinforcement Learning with the Q-Network

Lecture 1: Reinforcement Learning and Q-Networks

Lecture 2: Training a Neural Network with Q-Learning Part 1

Lecture 3: Training a Neural Network with Q-Learning Part 2

Lecture 4: Training a Neural Network with Q-Learning Part 3

Lecture 5: Challenge

Lecture 6: Extra Readings

Chapter 7: ML-Agents

Lecture 1: Read This First

Chapter 8: Unitys ML-Agents V0.3 [DEPRECATED]

Lecture 1: Setup

Lecture 2: Training Your First ML-Agent V0.3

Lecture 3: Migrating from V0.2 to V0.3

Lecture 4: ML-Agents FAQ

Lecture 5: Creating an ML-Agent From Scratch Part 1

Lecture 6: Creating an ML-Agent From Scratch Part 2

Lecture 7: ML-Agents Cheat Sheet

Lecture 8: An Avoiding ML-Agent Part 1

Lecture 9: An Avoiding ML-Agent Part 2

Lecture 10: Challenge

Lecture 11: Top 10 Tips for Neural Network Best Practice

Lecture 12: Environment Sensing ML-Agent

Lecture 13: Goal Seeking Wall Jumping Part 1

Lecture 14: Goal Seeking Wall Jumping Part 2

Lecture 15: Extra Readings

Chapter 9: Unitys M-Agents V0.2 [DEPRECIATED]

Lecture 1: About This Section

Lecture 2: Setting up TensorFlow – Starter Files

Lecture 3: Setting up TensorFlow – Windows

Lecture 4: Setting up TensorFlow – Mac

Lecture 5: An Overview of ML-Agents

Chapter 10: A Final Word

Lecture 1: Thank you

Lecture 2: Where to Now?

Instructors

Penny de Byl
International Award Winning Professor & Best Selling Author

Penny Holistic3D
Academic, Author & Game Development Enthusiast

Rating Distribution

1 stars: 13 votes

2 stars: 35 votes

3 stars: 163 votes

4 stars: 626 votes

5 stars: 1204 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!