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  1. Machine Learning Crash Course - Google Developers

    Google's fast-paced, practical introduction to machine learning, featuring a series of animated videos, interactive visualizations, and hands-on practice exercises.

  2. Machine Learning | Google for Developers

    Machine Learning Crash Course A hands-on course to explore the critical basics of machine learning.

  3. Linear regression | Machine Learning | Google for Developers

    Dec 9, 2025 · This course module teaches the fundamentals of linear regression, including linear equations, loss, gradient descent, and hyperparameter tuning.

  4. Machine Learning | Google for Developers

    Machine Learning Crash Course A hands-on course to explore the critical basics of machine learning.

  5. Prerequisites and prework | Machine Learning - Google Developers

    Aug 25, 2025 · While Machine Learning Crash Course may be useful to you as a refresher of fundamental machine learning concepts, you may also want to explore some of our advanced …

  6. Introduction to Machine Learning | Google for Developers

    Aug 25, 2025 · bookmark_border Welcome to Introduction to Machine Learning. This course introduces machine learning (ML) concepts. This course does not cover how to implement ML or work with data. …

  7. What is Machine Learning? | Google for Developers

    Sep 17, 2025 · Machine learning (ML) powers some of the most important technologies we use, from translation apps to autonomous vehicles. This course explains the core concepts behind ML.

  8. Machine Learning | Google for Developers

    Discover advanced courses about tools and techniques for solving machine learning problems.

  9. Introduction to Machine Learning Problem Framing

    Aug 25, 2025 · Introduction to Machine Learning Problem Framing teaches you how to determine if machine learning (ML) is a good approach for a problem and explains how to outline an ML solution.

  10. Neural networks | Machine Learning | Google for Developers

    Aug 25, 2025 · During training of a neural network, the model automatically learns the optimal feature crosses to perform on the input data to minimize loss. In the following sections, we'll take a closer …