Machine Learning For Dummies (eBook)

John Paul Mueller, Luca Massaron (Autoren)

eBook Download: PDF

2021 | 2. Auflage
John Wiley & Sons (Verlag)
978-1-119-72406-3 (ISBN)

Infos

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.

Autor

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.

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 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

Produkt-Details

• Erscheint lt. Verlag: 7.1.2021
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Theorie / Studium
• Schlagworte: AI • Anaconda • Artificial Intelligence • Big Data • Computer Science • Computer Science - General Interest • Informatik • linear models • machine learning • Machine Learning Models • machine learning tools • Maschinelles Lernen • Populäre Themen i. d. Informatik • Python • Python distribution • R distribution • R language • R Studio • Support Vector Machines • using big data
• ISBN-10: 1-119-72406-6 / 1119724066
• ISBN-13: 978-1-119-72406-3 / 9781119724063