Details
Machine Learning For Dummies
2. Aufl.
22,99 € |
|
Verlag: | Wiley |
Format: | |
Veröffentl.: | 07.01.2021 |
ISBN/EAN: | 9781119724063 |
Sprache: | englisch |
Anzahl Seiten: | 464 |
DRM-geschütztes eBook, Sie benötigen z.B. Adobe Digital Editions und eine Adobe ID zum Lesen.
Beschreibungen
One of Mark Cuban’s top reads for better understanding A.I. (inc.com, 2021) <p><b>Your comprehensive entry-level guide to machine learning</b></p> <p>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.</p> <p>Unlike most machine learning books, the fully updated 2nd Edition of Machine <i>Learning For Dummies</i> 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.</p> <ul> <li>Understand the history of AI and machine learning</li> <li>Work with Python 3.8 and TensorFlow 2.x (and R as a download)</li> <li>Build and test your own models</li> <li>Use the latest datasets, rather than the worn out data found in other books</li> <li>Apply machine learning to real problems</li> </ul> <p>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.</p>
<p>Introduction 1</p> <p><b>Part 1: Introducing How Machines Learn 5</b></p> <p>Chapter 1: Getting the Real Story about AI 7</p> <p>Chapter 2: Learning in the Age of Big Data 23</p> <p>Chapter 3: Having a Glance at the Future 37</p> <p><b>Part 2: Preparing Your Learning Tools 47</b></p> <p>Chapter 4: Installing a Python Distribution 49</p> <p>Chapter 5: Beyond Basic Coding in Python 67</p> <p>Chapter 6: Working with Google Colab 87</p> <p><b>Part 3: Getting Started with the Math Basics 115</b></p> <p>Chapter 7: Demystifying the Math Behind Machine Learning 117</p> <p>Chapter 8: Descending the Gradient 139</p> <p>Chapter 9: Validating Machine Learning 153</p> <p>Chapter 10: Starting with Simple Learners 175</p> <p><b>Part 4: Learning from Smart and Big Data 197</b></p> <p>Chapter 11: Preprocessing Data 199</p> <p>Chapter 12: Leveraging Similarity 221</p> <p>Chapter 13: Working with Linear Models the Easy Way 243</p> <p>Chapter 14: Hitting Complexity with Neural Networks 271</p> <p>Chapter 15: Going a Step Beyond Using Support Vector Machines 307</p> <p>Chapter 16: Resorting to Ensembles of Learners 319</p> <p><b>Part 5: Applying Learning to Real Problems 339</b></p> <p>Chapter 17: Classifying Images 341</p> <p>Chapter 18: Scoring Opinions and Sentiments 361</p> <p>Chapter 19: Recommending Products and Movies 383</p> <p><b>Part 6: The Part of Tens 405</b></p> <p>Chapter 20: Ten Ways to Improve Your Machine Learning Models 407</p> <p>Chapter 21: Ten Guidelines for Ethical Data Usage 415</p> <p>Chapter 22: Ten Machine Learning Packages to Master 423</p> <p>Index 431</p>
<p><b>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.<BR> 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.</b></p>
<p><b>Fun ways to work and play with new machine learning tools</b> <p>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 <i>Machine Learning For Dummies,</i> 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. <p><b>Inside. . .</b> <li><bl>Intro to machine learning and AI</bl></li><li><bl> Big data and algorithms explained</bl></li><li><bl> Demystifying the math behind AI</bl></li><li><bl> Many best practice examples</bl></li><li><bl> Practical uses for machine learning</bl></li><li><bl> Real-world datasets</bl></li><li><bl> Ethical approaches to data use</bl></li>