Machine Learning For Dummies (häftad)
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Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
464
Utgivningsdatum
2021-04-08
Upplaga
2nd Edition
Förlag
John Wiley & Sons Inc
Dimensioner
234 x 185 x 28 mm
Vikt
613 g
Antal komponenter
1
ISBN
9781119724018
Machine Learning For Dummies (häftad)

Machine Learning For Dummies

Häftad Engelska, 2021-04-08
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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.
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De som köpt den här boken har ofta också köpt Python for Data Science For Dummies av John Paul Mueller, Luca Massaron (häftad).

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Övrig information

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.

Innehållsförteckning

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 Using Anaconda for Machine Learning 50 Getting Anaconda 50 Defining why Anaconda is used in this book 51 Installing Anaconda on Linux 52 Installing Anaconda on Mac OS X 53 Installing Anaconda on Windows 54 Downloading the Datasets and Example Code 57 Using Jupyter Notebook 57 Defining the code repository 59 Understanding the datasets used in this book 64 Chapter 5: Beyond Basic Coding in Python 67 Defining the Basics You Should Know 68 Considering Python basics 68 Working with functions 72 Working with modules 76 Storing Data Using Sets, Lists, and Tuples 78 Creating sets 78 Performing operations on sets 78 Using lists 79 Creating and using tuples 82 Defining Useful Iterators 83 Working with ranges 83 Iterating multiple lists using zip 84 Working with generators using yield 84 Indexing Data Using Dictionaries 85 Creating dictionaries 85 Storing and retrieving data from dictionaries 85 Chapter 6: Working with Google Colab 87 Defining Google Colab 88 Understanding what Google Colab does 88 Considering the online coding difference 90 Using local runtime support 91 Working with Google Colab features 91 Getting a Google Account 94 Creating the account 94 Signing in 95 Working with Notebooks 96 Creating a new notebook 96 Opening existing notebooks 97 Uploading a notebook 99 Saving notebooks 100 Downloading notebooks 103 Performing Common Tasks 103 Creating code cells 104 Creating text cells 106 Creating special cells 107 Editing cells 108 Moving cells 108 Using Hardware Acceleration 108 Viewing Your Notebook 109 Displaying the table of contents 110 Getting notebook information 110 Checking code execution 110 Executing the Code 111 Sharing Your Notebook 112 Getting Help 113 Part 3: Getting Started with the Math Basics 115 Chapter 7: Demystifying the Math Behind Machin