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Machine Learning For Dummies

Luca Massaron (Author), John Paul Mueller (Author)

9781394373222, Wiley

Paperback / softback, published 20 November 2025

448 pages
23.4 x 18.8 x 3 cm, 0.612 kg

The most human-friendly book on machine learning

Somewhere buried in all the systems that drive artificial intelligence, you'll find machine learning—the process that allows technology to build knowledge based on data and patterns. Machine Learning For Dummies is an excellent starting point for anyone who wants deeper insight into how all this learning actually happens. This book offers an overview of machine learning and its most important practical applications. Then, you'll dive into the tools, code, and math that make machine learning go—and you'll even get step-by-step instructions for testing it out on your own. For an easy-to-follow introduction to building smart algorithms, this Dummies guide is your go-to.

  • Piece together what machine learning is, what it can do, and what it can't do
  • Learn the basics of machine learning code and how it integrates with large datasets
  • Understand the mathematical principles that AI uses to make itself smarter
  • Consider real-world applications of machine learning and write your own algorithms

With clear explanations and hands-on instruction, Machine Learning For Dummies is a great entry-level resource for developers looking to get started with AI and machine learning.

Introduction 1

Part 1: Introducing How Machines Learn 5

Chapter 1: Getting the Real Story About AI 7

Chapter 2: Learning in the Age of Computers 23

Chapter 3: Having a Glance at the Future 35

Part 2: Learning Machine Learning by Coding 45

Chapter 4: Working with Google Colab 47

Chapter 5: Understanding the Tools of the Trade 71

Chapter 6: Getting Beyond Basic Coding in Python 81

Part 3: Building the Foundations 103

Chapter 7: Demystifying the Math Behind Machine Learning 105

Chapter 8: Descending the Gradient 129

Chapter 9: Validating Machine Learning 145

Part 4: Learning from Smart Algorithms 169

Chapter 10: Starting with Simple Learners 171

Chapter 11: Leveraging Similarity 195

Chapter 12: Working with Linear Models the Easy Way 219

Chapter 13: Going Beyond the Basics with Support Vector Machines 251

Chapter 14: Tackling Complexity with Neural Networks 263

Chapter 15: Resorting to Ensembles of Learners 303

Part 5: Applying Learning to Real Problems 327

Chapter 16: Classifying Images 329

Chapter 17: Scoring Opinions and Sentiments 351

Chapter 18: Recommending Products and Movies 379

Part 6: The Part of Tens 401

Chapter 19: Ten Ways to Improve Your Machine Learning Models 403

Chapter 20: Ten Guidelines for Ethical Data Usage 411

Index 419

Subject Areas: Computer science [UY]
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