Official Machine Learning Education Help Center where you can find tips and tutorials on using Machine Learning Education and other answers to frequently asked questions.| support.google.com
This course module teaches key considerations and best practices for putting an ML model into production, including static vs. dynamic training, static vs. dynamic inference, transforming data, and deployment testing and monitoring.| Google for Developers
Is Machine Learning Crash Course right for you?| Google for Developers
This course module provides guidelines for preparing data for machine learning model training, including how to identify unreliable data; how to discard and impute data; how to improve labels; how to split data into training, validation and test sets; and how to prevent overfitting and ensure models can generalize using regularization techniques.| Google for Developers
This course module teaches fundamental concepts and best practices for working with numerical data, from how data is ingested into a model using feature vectors to feature engineering techniques such as normalization, binning, scrubbing, and creating synthetic features with polynomial transforms.| Google for Developers
This course module teaches the basics of neural networks: the key components of neural network architectures (nodes, hidden layers, activation functions), how neural network inference is performed, how neural networks are trained using backpropagation, and how neural networks can be used for multi-class classification problems.| Google for Developers
This course module teaches the fundamentals of logistic regression, including how to predict a probability, the sigmoid function, and Log Loss.| Google for Developers
This course module provides an overview of language models and large language models (LLMs), covering concepts including tokens, n-grams, Transformers, self-attention, distillation, fine-tuning, and prompt engineering.| Google for Developers
This course module teaches the fundamentals of linear regression, including linear equations, loss, gradient descent, and hyperparameter tuning.| Google for Developers
This course module teaches key principles of ML Fairness, including types of human bias that can manifest in ML models, identifying and mitigating these biases, and evaluating for these biases using metrics including demographic parity, equality of opportunity, and counterfactual fairness.| Google for Developers
This course module teaches the key concepts of embeddings, and techniques for training an embedding to translate high-dimensional data into a lower-dimensional embedding vector.| Google for Developers
This course module teaches the fundamentals of binary classification, including thresholding, the confusion matrix, and classification metrics such as accuracy, precision, recall, ROC, AUC, and prediction bias. A brief intro to multi-class classification is provided at the end of the module.| Google for Developers
This course module teaches the fundamental concepts and best practices of working with categorical data, including encoding methods such as one-hot encoding and hashing, creating feature crosses, and common pitfalls to look out for.| Google for Developers
This course module teaches best practices for using automated machine learning (AutoML) tools in your machine learning workflow, including benefits and limitations and common AutoML patterns that can be used in projects.| Google for Developers
