Verkauf durch Sack Fachmedien

Massaron / Mueller

Machine Learning For Dummies

Medium: Buch
ISBN: 978-1-394-37322-2
Verlag: Wiley John + Sons
Erscheinungstermin: 21.11.2025
Nicht mehr lieferbar

The most human-friendly book on machine learning

Somewhere buried in all the systems that drive artificial intelligence, you'll find machine learning—the process that allows technology to build knowledge based on data and patterns. Machine Learning For Dummies is an excellent starting point for anyone who wants deeper insight into how all this learning actually happens. This book offers an overview of machine learning and its most important practical applications. Then, you'll dive into the tools, code, and math that make machine learning go—and you'll even get step-by-step instructions for testing it out on your own. For an easy-to-follow introduction to building smart algorithms, this Dummies guide is your go-to. - Piece together what machine learning is, what it can do, and what it can't do
- Learn the basics of machine learning code and how it integrates with large datasets
- Understand the mathematical principles that AI uses to make itself smarter
- Consider real-world applications of machine learning and write your own algorithms

With clear explanations and hands-on instruction, Machine Learning For Dummies is a great entry-level resource for developers looking to get started with AI and machine learning.


Produkteigenschaften


  • Artikelnummer: 9781394373222
  • Medium: Buch
  • ISBN: 978-1-394-37322-2
  • Verlag: Wiley John + Sons
  • Erscheinungstermin: 21.11.2025
  • Sprache(n): Englisch
  • Auflage: 3rd Auflage
  • Serie: For Dummies
  • Produktform: Kartoniert
  • Gewicht: 813 g
  • Seiten: 448
  • Format (B x H x T): 186 x 233 x 27 mm
  • Ausgabetyp: Kein, Unbekannt
  • Vorauflage: 978-1-119-72401-8
Autoren/Hrsg.

Autoren

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 Computers 23 Chapter 3: Having a Glance at the Future 35 Part 2: Learning Machine Learning by Coding 45 Chapter 4: Working with Google Colab 47 Chapter 5: Understanding the Tools of the Trade 71 Chapter 6: Getting Beyond Basic Coding in Python 81 Part 3: Building the Foundations 103 Chapter 7: Demystifying the Math Behind Machine Learning 105 Chapter 8: Descending the Gradient 129 Chapter 9: Validating Machine Learning 145 Part 4: Learning from Smart Algorithms 169 Chapter 10: Starting with Simple Learners 171 Chapter 11: Leveraging Similarity 195 Chapter 12: Working with Linear Models the Easy Way 219 Chapter 13: Going Beyond the Basics with Support Vector Machines 251 Chapter 14: Tackling Complexity with Neural Networks 263 Chapter 15: Resorting to Ensembles of Learners 303 Part 5: Applying Learning to Real Problems 327 Chapter 16: Classifying Images 329 Chapter 17: Scoring Opinions and Sentiments 351 Chapter 18: Recommending Products and Movies 379 Part 6: The Part of Tens 401 Chapter 19: Ten Ways to Improve Your Machine Learning Models 403 Chapter 20: Ten Guidelines for Ethical Data Usage 411 Index 419