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  • Book Overview & Buying Machine Learning For Dummies
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Machine Learning For Dummies

Machine Learning For Dummies

By : John Paul Mueller, Luca Massaron
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Machine Learning For Dummies

Machine Learning For Dummies

By: John Paul Mueller, Luca Massaron

Overview of this book

Machine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn’t be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks. In the initial chapters, the book introduces you to the world of machine learning, artificial intelligence, big data, and will prepare you to use R and Python for machine learning tasks. Next, you’ll learn how to use math in machine learning and get started with linear models and neural networks. In the final chapters, you’ll process images and text, and discover packages and techniques to improve your machine learning models. By the end of this book, you’ll be able to understand and implement machine learning seamlessly.
Table of Contents (34 chapters)
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2
Part 1: Introducing How Machines Learn
6
Part 2: Preparing Your Learning Tools
12
Part 3: Getting Started with the Math Basics
17
Part 4: Learning from Smart and Big Data
24
Part 5: Applying Learning to Real Problems
28
Part 6: The Part of Tens
31
About the Author
32
Advertisement Page
33
Connect with Dummies
34
End User License Agreement

Accessing Complex Algorithms Easily Using LIBSVM

Chapter 17 helps you discover the wonders of support vector machines (SVMs). LIBSVM (https://www.csie.ntu.edu.tw/~cjlin/libsvm/) is a library of SVMs that you can use to perform tasks such as the following:

  • Cross-validation for model selection
  • Probability estimates
  • Modeling unbalanced data
  • Multiclass classification

An advantage of LIBSVM is that it relies on extensions to provide various types of support. Consequently, you have access to LIBSVM through a huge number of languages: Python, R, MATLAB, Perl, Ruby, Weka, Common LISP, CLISP, Haskell, OCaml, LabVIEW, PHP, C# .NET, and CUDA. Supporters have also created a wealth of tools for the library, including an easy script interface for users who know nothing about SVM. (You need Python and gnuplot installed to obtain easy script support.)

tip If you scroll halfway down the support page, you can find the Graphic Interface section, where you can see LIBSVM in action. The example program is a...

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Tech Concepts
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Programming languages
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Machine Learning For Dummies
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