Ready to start practicing machine learning? Learn and apply fundamental machine learning concepts with the Crash Course, get real-world experience with the companion Kaggle competition, or visit Learn with Google AI to explore the full library of training resources.

Evaluating a machine learning model responsibly requires doing more than just calculating loss metrics. Before putting a model into production, it's critical to audit training data and evaluate predictions for bias. This module looks at different types of human biases that can manifest in training data. It then provides strategies to identify them and evaluate their effects.

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How does machine learning differ from traditional programming? What is loss, and how do I measure it? How does gradient descent work? How do I determine whether my model is effective? How do I represent my data so that a program can learn from it? How do I build a deep neural network?