Introduction to Machine Learning

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developers.google.com/machine-learning/intro-to-ml

Learning objectives

• Understand the different types of machine learning.
• Understand the key concepts of supervised machine learning.
• Learn how solving problems with ML is different from traditional approaches.

Definition of ML

In basic terms, ML is the process of training a piece of software, called a model, to make useful predictions or generate content from data.

Types of ML Systems

• Supervised learning
  • Regression model
    • Linear regression
    • Logistic regression
  • Classification model
    • Binary classification
    • Multi-class classification
• Unsupervised learning
• Reinforcement learning
• Generative AI

What’s Next

• Introduction to machine learning problem framing: If you’re looking for a field-tested approach for creating ML models and avoiding common pitfalls along the way.
• People + AI guidebook: If you’re looking for a set of methods, best practices and examples presented by Googlers, industry experts, and academic research for using ML.
• Machine learning crash course: If you’re ready for an in-depth and hands-on approach to learning more about ML.