Foundational courses

The foundational courses cover machine learning fundamentals and core concepts.

We recommend taking them in the order below.

A brief introduction to machine learning.
A hands-on course to explore the critical basics of machine learning.
A course to help you map real-world problems to machine learning solutions.
An introduction to preparing your data for ML workflows.
Strategies for testing and debugging machine learning models and pipelines.
Take more ML courses to improve your knowledge and skills.

Advanced courses

The advanced courses teach tools and techniques for solving a variety of machine learning problems.

The courses are structured independently. Take them based on interest or problem domain.

Decision forests are an alternative to neural networks.
Recommendation systems generate personalized suggestions.
Clustering is a key unsupervised machine learning strategy to associate related items.
GANs create new data instances that resemble your training data.
Is that a picture of a cat or is it a dog?
Hands-on practice debugging fairness issues.


Our guides offer simple step-by-step walkthroughs for solving common machine learning problems using best practices.
Become a better machine learning engineer by following these machine learning best practices used at Google.
This guide assists UXers, PMs, and developers in collaboratively working through AI design topics and questions.
This comprehensive guide provides a walkthrough to solving text classification problems using machine learning.
This guide describes the tricks that an expert data analyst uses to evaluate huge data sets in machine learning problems.
This guide explains a scientific way to optimize the training of deep learning models.


The glossaries define machine learning terms.
ML fundamental terms and definitions.
Decision forest key terms and definitions.
Clustering key terms and definitions.
Full glossary containing all definitions.