John Mueller has produced hundreds of books and articles on topics ranging from networking to home security and from database management to heads-down programming.
Luca Massaron is a senior expert in data science who has been involved with quantitative methods since 2000. He is a Google Developer Expert (GDE) in 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 Big Data 23
 
Chapter 3: Having a Glance at the Future 37
 
Part 2: Preparing Your Learning Tools 47
 
Chapter 4: Installing a Python Distribution 49
 
Chapter 5: Beyond Basic Coding in Python 67
 
Chapter 6: Working with Google Colab 87
 
Part 3: Getting Started with the Math Basics 115
 
Chapter 7: Demystifying the Math Behind Machine Learning 117
 
Chapter 8: Descending the Gradient 139
 
Chapter 9: Validating Machine Learning 153
 
Chapter 10: Starting with Simple Learners 175
 
Part 4: Learning from Smart and Big Data 197
 
Chapter 11: Preprocessing Data 199
 
Chapter 12: Leveraging Similarity 221
 
Chapter 13: Working with Linear Models the Easy Way 243
 
Chapter 14: Hitting Complexity with Neural Networks 271
 
Chapter 15: Going a Step Beyond Using Support Vector Machines 307
 
Chapter 16: Resorting to Ensembles of Learners 319
 
Part 5: Applying Learning to Real Problems 339
 
Chapter 17: Classifying Images 341
 
Chapter 18: Scoring Opinions and Sentiments 361
 
Chapter 19: Recommending Products and Movies 383
 
Part 6: The Part of Tens 405
 
Chapter 20: Ten Ways to Improve Your Machine Learning Models 407
 
Chapter 21: Ten Guidelines for Ethical Data Usage 415
 
Chapter 22: Ten Machine Learning Packages to Master 423
 
Index 431

One of Mark Cuban's top reads for better understanding A.I. (inc.com, 2021)
 
Your comprehensive entry-level guide to machine learning
 
While machine learning expertise doesn't quite mean you can create your own Turing Test-proof android--as in the movie Ex Machina--it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving problems fast and on a large scale. Anyone who masters the principles of machine learning is mastering a big part of our tech future and opening up incredible new directions in careers that include fraud detection, optimizing search results, serving real-time ads, credit-scoring, building accurate and sophisticated pricing models--and way, way more.
 
Unlike most machine learning books, the fully updated 2nd Edition of Machine Learning For Dummies doesn't assume you have years of experience using programming languages such as Python (R source is also included in a downloadable form with comments and explanations), but lets you in on the ground floor, covering the entry-level materials that will get you up and running building models you need to perform practical tasks. It takes a look at the underlying--and fascinating--math principles that power machine learning but also shows that you don't need to be a math whiz to build fun new tools and apply them to your work and study.
* Understand the history of AI and machine learning
* Work with Python 3.8 and TensorFlow 2.x (and R as a download)
* Build and test your own models
* Use the latest datasets, rather than the worn out data found in other books
* Apply machine learning to real problems
 
Whether you want to learn for college or to enhance your business or career performance, this friendly beginner's guide is your best introduction to machine learning, allowing you to become quickly confident using this amazing and fast-developing technology that's impacting lives for the better all over the world.