Autorentext

Luca Massaron is a data science, machine learning, and artificial intelligence expert. He's the author of Artificial Intelligence For Dummies, Deep Learning For Dummies, and Machine Learning For Dummies. John Paul Mueller was a long-time tech author whose credits include previous editions of this book along with Artificial Intelligence For Dummies and Algorithms For Dummies.

Klappentext

An accessible guide to machine learning Machine learning is the process that drives the world's most powerful artificial intelligence systems. This book walks you through exactly how this exciting discipline actually works. You'll learn all about the tools, code, and math behind machine learning. You'll also find step-by-step instructions for testing it out on your own and an easy-to-follow introduction to building smart algorithms. This is your guide to understanding how to build predictive models, detect anomalies, analyze text and images, and more. Inside ... **

• Understand the basics
• Learn machine learning code
• Demystify the math
• Build real-world algorithms
• Explore examples and apps
• Work with large datasets
• Access effective tools
• Test out your creations**

Inhalt

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