A Beginner's Guide To Machine Learning with Unity

Advanced games AI with genetic algorithms, neural networks & Q-learning in C# and Tensorflow for Unity

A Beginner's Guide To Machine Learning with Unity

InstructorPenny de Byl

Duration13h 3m

Students25.3K

Rating4.6 (2,153)

Comidoc Analysis

C# Algorithmic Foundations vs. Deprecated Unity Tooling

Strengths

Algorithmic Implementation Path

The structure moves logically from genetic algorithms to individual perceptrons, then to full artificial neural networks and reinforcement learning.

Best suited to

Game developers seeking to build neural networks from scratch in C#
Learners interested in the mathematical foundations of genetic algorithms
Unity users wanting to understand reinforcement learning through practical exercises

Less suited to

Developers requiring up-to-date Unity ML-Agents workflows
Learners who prefer highly legible presentation slides

Editorial course preview

What the public course preview actually shows

These 4 complementary views highlight concrete, legible examples from the course presentation.

Split-screen Unity Editor view showing a population of humanoid agents on a red platform in Scene and Game views with generation statistics displayed — Preview 1 of 4
Unity Editor split-screen view displaying a genetic algorithm simulation with humanoid agents on a red platform, showing scene perspective and game view with population statistics.

A schematic diagram of a neural network showing an input layer with two nodes, two hidden layers with four nodes each, and an output layer with one node, connected by yellow arrows — Preview 2 of 4
This diagram illustrates the architecture of a neural network, featuring an input layer, two hidden layers, and an output layer to visualize data flow.

A composite screenshot showing the Unity Editor interface with a Pong game simulation in the Game view, C# source code for an AI agent's brain script, and a 3D model of a paddle. — Preview 3 of 4
This image displays the Unity Editor environment featuring a Pong game simulation alongside the corresponding C# neural network code used to train the AI agent.

A presentation slide on a black background defining Q-learning with mathematical formulas for Quality of Performing Action and Bellman's Equation. — Preview 4 of 4
This educational slide defines Q-learning by presenting the formula for the quality of performing an action alongside Bellman's equation for calculating maximum future rewards.