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.

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.

Fun ways to work and play with new machine learning tools

What, exactly, is machine learning? How can you implement it, and which tools will you need? This book shows you how to build predictive models, detect anomalies, analyze text and images, and more. Machine learning makes all this possible. Dive into this exciting new technology with Machine Learning For Dummies, 2nd Edition. This even-friendlier new edition answers your questions guiding you in learning essential programming and concepts from scratch! Here is the entry-level info you need to get up and running with machine learning.

Inside. . .

Your comprehensive entry-level guide to machine learning

While machine learning expertise doesn't quite mean you can create your own Turing Test-proof androidas in the movie Ex Machinait 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 modelsand 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 underlyingand fascinatingmath 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.

Introduction 1

About This Book 1

Foolish Assumptions 2

Icons Used in This Book 3

Beyond the Book 3

Where to Go from Here 4

Part 1: Introducing How Machines Learn 5

Chapter 1: Getting the Real Story about AI 7

Moving beyond the Hype 8

Dreaming of Electric Sheep 9

Understanding the history of AI and machine learning 10

Exploring what machine learning can do for AI 11

Considering the goals of machine learning 12

Defining machine learning limits based on hardware 12

Overcoming AI Fantasies 13

Discovering the fad uses of AI and machine learning 14

Considering the true uses of AI and machine learning 15

Being useful; being mundane 16

Considering the Relationship between AI and Machine Learning 17

Considering AI and Machine Learning Specifications 18

Defining the Divide between Art and Engineering 19

Predicting the Next AI Winter 20

Chapter 2: Learning in the Age of Big Data 23

Considering the Machine Learning Essentials 24

Defining Big Data 25

Considering the Sources of Big Data 26

Building a new data source 26

Using existing data sources 29

Locating test data sources 29

Specifying the Role of Statistics in Machine Learning 30

Understanding the Role of Algorithms 31

Defining what algorithms do 32

Considering the five main techniques 32

Defining What Training Means 34

Chapter 3: Having a Glance at the Future 37

Creating Useful Technologies for the Future 38

Considering the role of machine learning in robots 38

Using machine learning in health care 39

Creating smart systems for various needs 40

Using machine learning in industrial settings 40

Understanding the role of updated processors and other hardware 41

Discovering the New Work Opportunities with Machine Learning 42

Working for a machine 42

Working with machines 43

Repairing machines 44

Creating new machine learning tasks 44

Devising new machine learning environments 45

Avoiding the Potential Pitfalls of Future Technologies 46

Part 2: Preparing Your Learning Tools 47

Chapter 4: Installing a Python Distribution 49<…