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

4.55 (2086 reviews)

Udemy

platform

English

language

Data Science

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.

Course Gallery

A Beginner's Guide To Machine Learning with Unity – Screenshot 1 — Screenshot 1 – A Beginner's Guide To Machine Learning with Unity

A Beginner's Guide To Machine Learning with Unity – Screenshot 2 — Screenshot 2 – A Beginner's Guide To Machine Learning with Unity

A Beginner's Guide To Machine Learning with Unity – Screenshot 3 — Screenshot 3 – A Beginner's Guide To Machine Learning with Unity

A Beginner's Guide To Machine Learning with Unity – Screenshot 4 — Screenshot 4 – A Beginner's Guide To Machine Learning with Unity

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Rating & Reviews

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Comidoc Review

Our Verdict

A Beginner's Guide To Machine Learning with Unity offers a solid foundation in genetic algorithms and neural networks, enabling students to apply machine learning concepts to Unity projects. While some outdated content and code readability issues detract from the overall experience, this course remains valuable for those wanting to learn through practical, hands-on examples in C#.

What We Liked

In-depth coverage of genetic algorithms and neural networks, with a focus on practical implementation in C#.
Exploration of Unity ML-Agents plugin and Tensorflow for training game characters.
Thorough instruction on integrating contemporary research ideas into personal projects.
Comprehensive guidance on distilling the mathematics and statistics behind machine learning into working program code.

Potential Drawbacks

Variable naming conventions can make the Neural Network sections harder to understand.
Some outdated ML-Agents example code could benefit from occasional updates, even if videos aren't changed.
Certain color scheme issues in Monodevelop may cause difficulties with reading red spectrum colors.
A significant portion of the course is deprecated and does not cover Unity ML agents.