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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. 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

    4 days ago · Prerequisites: This module assumes you are familiar with the concepts covered in the following module: Introduction to Machine Learning Linear regression is a statistical technique used …

  4. Machine Learning & Artificial Intelligence Basics - Google Developers

    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 become integral to …

  5. Prerequisites and prework | Machine Learning - Google Developers

    Aug 25, 2025 · Please read through the following Prework and Prerequisites sections before beginning Machine Learning Crash Course, to ensure you are prepared to complete all the modules.

  6. Machine Learning | Google for Developers

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

  7. Machine Learning | Google for Developers

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

  8. 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. ML …

  9. 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.

  10. Numerical data: How a model ingests data using feature vectors

    Aug 25, 2025 · Instead, you must typically process the dataset's values into representations that your model can better learn from. So, a more realistic feature vector might look something like this: