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  1. Machine Learning | Google for Developers

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

  2. 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 …

  3. Linear regression | Machine Learning | Google for Developers

    Aug 25, 2025 · Prerequisites: This module assumes you are familiar with the concepts covered in the following module: Introduction to Machine Learning Linear regression is a statistical …

  4. Machine Learning | Google for Developers

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

  5. Machine Learning | Google for Developers

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

  6. Machine Learning & Artificial Intelligence Basics - Google …

    Aug 25, 2025 · Machine learning (ML) is the field of study of programs or systems that trains models to make predictions from input data. ML powers some of the technologies that have …

  7. Classification: Accuracy, recall, precision, and related metrics ...

    Aug 25, 2025 · Learn how to calculate three key classification metrics—accuracy, precision, recall—and how to choose the appropriate metric to evaluate a given binary classification model.

  8. Neural networks | Machine Learning | Google for Developers

    Aug 25, 2025 · By adding the feature cross x1x2, the linear model can learn a hyperbolic shape that separates the blue dots from the orange dots. Now consider the following dataset:

  9. Machine Learning | Google for Developers

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

  10. Introduction to Machine Learning | Google for Developers

    Aug 25, 2025 · Learn about the types of ML, supervised ML, and how solving problems with ML differs from traditional approaches.