Introduction to Machine Learning with Python
A Guide for Data Scientists
Publisher: O'Reilly Media
Final Release Date: September 2016
Pages: 394

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.

You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.

With this book, you’ll learn:

  • Fundamental concepts and applications of machine learning
  • Advantages and shortcomings of widely used machine learning algorithms
  • How to represent data processed by machine learning, including which data aspects to focus on
  • Advanced methods for model evaluation and parameter tuning
  • The concept of pipelines for chaining models and encapsulating your workflow
  • Methods for working with text data, including text-specific processing techniques
  • Suggestions for improving your machine learning and data science skills
Table of Contents
Product Details
About the Author
Colophon
Recommended for You
Customer Reviews

REVIEW SNAPSHOT®

by PowerReviews
oreillyIntroduction to Machine Learning with Python
 
5.0

(based on 2 reviews)

Ratings Distribution

  • 5 Stars

     

    (2)

  • 4 Stars

     

    (0)

  • 3 Stars

     

    (0)

  • 2 Stars

     

    (0)

  • 1 Stars

     

    (0)

Reviewed by 2 customers

Displaying reviews 1-2

Back to top

(2 of 3 customers found this review helpful)

 
5.0

Best no-math book on Machine Learning

By Anton

from Helsinki

About Me Developer, Educator

Verified Buyer

Pros

  • Accurate
  • Concise
  • Easy to understand
  • Helpful examples
  • Well-written

Cons

    Best Uses

    • Intermediate
    • Novice
    • Student

    Comments about oreilly Introduction to Machine Learning with Python:

    Excellent book that tells how to do Machine Learning without going into algebra or differential equations. Very clear explanation of test/validation procedure, parameter selection, pipelines. Highly recommended for non-unversity education or self study!

    (5 of 5 customers found this review helpful)

     
    5.0

    A good hands-on intro to sklearn

    By partsiartsi

    from Finland

    About Me Developer

    Pros

    • Accurate
    • Concise
    • Helpful examples
    • Well-written

    Cons

      Best Uses

      • Expert
      • Intermediate
      • Novice

      Comments about oreilly Introduction to Machine Learning with Python:

      The sklearn library is great, but if you lack a solid bacground in machine learning / statistics it can be hard to find the relevant modules or good practical examples on how to use them. This book provides a great intro to basic machine learning in data analysis with practical code examples.

      You should be somewhat familiar with numpy and matplotlib (and perhaps scipy/pandas etc) to fluently read this book, but a short intro to these is provided. The "Python data science handbook" looks good if you lack the background (though still in early release).

      Displaying reviews 1-2

      Back to top

       
      Buy 2 Get 1 Free Free Shipping Guarantee
      Buying Options
      Immediate Access - Go Digital what's this?
      Ebook:  $42.99
      Formats:  DAISY, ePub, Mobi, PDF
      Print & Ebook:  $54.99
      Print:  $49.99