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Full Description
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.
Contents
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
