Editor's Synopsis
Google's Machine Learning Crash Course (MLCC) has been refreshed with updated content covering recent advances in AI, including new modules on large language models (LLMs) and AutoML. Since its original launch in 2018, millions of learners worldwide have used MLCC as a foundational resource for understanding machine learning concepts and applications. The updated course emphasizes interactive learning and spans a comprehensive curriculum.
The course covers core ML topics including linear regression, logistic regression, classification, neural networks, and embeddings, along with practical modules on working with numerical and categorical data, datasets and generalization, and overfitting. Newer additions address Transformers and LLM architecture, production ML systems, automated machine learning best practices, and ML fairness — including strategies for identifying and mitigating biases in data. The course is designed to take learners from fundamental concepts like gradient descent and hyperparameter tuning through to production-level considerations.
