Machine Learning Crash Course
Google's fast-paced, practical introduction to machine learning, featuring a series of lessons with video lectures, interactive visualizations, and hands-on practice exercises.
100+ exercises
12 modules
15 hours
Video explainers of ML concepts
Real-world examples
Interactive visualizations
Course Modules
Each Machine Learning Crash Course module is self-contained, so if you have prior experience in machine learning, you can skip directly to the topics you want to learn. If you're new to machine learning, we recommend completing modules in the order below.
ML Models
These modules cover the fundamentals of building regression and classification models.
Linear Regression
An introduction to linear regression, covering linear models, loss, gradient descent, and hyperparameter tuning.
Logistic Regression
An introduction to logistic regression, where ML models are designed to predict the probability of a given outcome.
Classification
An introduction to binary classification models, covering thresholding, confusion matrices, and metrics like accuracy, precision, recall, and AUC.
Data
These modules cover fundamental techniques and best practices for working with machine learning data.
Working with Numerical Data
Learn how to analyze and transform numerical data to help train ML models more effectively.
Working with Categorical Data
Learn the fundamentals of working with categorical data: how to distinguish categorical data from numerical data; how to represent categorical data numerically using one-hot encoding, feature hashing, and mean encoding; and how to perform feature crosses.
Datasets, Generalization, and Overfitting
An introduction to the characteristics of machine learning datasets, and how to prepare your data to ensure high-quality results when training and evaluating your model.
Advanced ML models
These modules cover advanced ML model architectures.
Neural Networks
An introduction to the fundamental principles of neural network architectures, including perceptrons, hidden layers, and activation functions.
Embeddings
Learn how embeddings allow you to do machine learning on large feature vectors.
New
Large Language Models
An introduction to large language models, from tokens to Transformers. Learn the basics of how LLMs learn to predict text output, as well as how they're architected and trained.
Real-world ML
These modules cover critical considerations when building and deploying ML models in the real world, including productionization best practices, automation, and responsible engineering.
Production ML Systems
Learn how a machine learning production system works across a breadth of components.
ML Fairness
Learn principles and best practices for auditing ML models for fairness, including strategies for identifying and mitigating biases in data.