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Michael Bowles
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Machine Learning in Python: Essential Techniques for Predictive Analysis Paperback – 17 April 2015

by Michael Bowles (Author)
4.3 4.3 out of 5 stars 33 ratings
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Machine Learning with Spark and Python: Essential Techniques for Predictive Analytics
Machine Learning with Spark and Python: Essential Techniques for Predictive Analytics
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Learn a simpler and more effective way to analyze data and predict outcomes with Python

Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, data preparation, and using the trained models in practice. You will learn a core set of Python programming techniques, various methods of building predictive models, and how to measure the performance of each model to ensure that the right one is used. The chapters on penalized linear regression and ensemble methods dive deep into each of the algorithms, and you can use the sample code in the book to develop your own data analysis solutions.

Machine learning algorithms are at the core of data analytics and visualization. In the past, these methods required a deep background in math and statistics, often in combination with the specialized R programming language. This book demonstrates how machine learning can be implemented using the more widely used and accessible Python programming language.

  • Predict outcomes using linear and ensemble algorithm families
  • Build predictive models that solve a range of simple and complex problems
  • Apply core machine learning algorithms using Python
  • Use sample code directly to build custom solutions

Machine learning doesn't have to be complex and highly specialized. Python makes this technology more accessible to a much wider audience, using methods that are simpler, effective, and well tested. Machine Learning in Python shows you how to do this, without requiring an extensive background in math or statistics.

Previous page
  1. Print length
    360 pages
  2. Language
    English
  3. Publisher
    Wiley
  4. Publication date
    17 April 2015
  5. Dimensions
    18.29 x 1.78 x 23.11 cm
  6. ISBN-10
    1118961749
  7. ISBN-13
    978-1118961742
  8. See all details
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Product description

About the Author

MICHAEL BOWLES teaches machine learning at Hacker Dojo in Silicon Valley, consults on machine learning projects, and is involved in a number of startups in such areas as bioinformatics and high-frequency trading. Following an assistant professorship at MIT, Michael went on to found and run two Silicon Valley startups, both of which went public. His courses at Hacker Dojo are nearly always sold out and receive great feedback from participants.

Product details

  • Publisher ‏ : ‎ Wiley; 1st edition (17 April 2015)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 360 pages
  • ISBN-10 ‏ : ‎ 1118961749
  • ISBN-13 ‏ : ‎ 978-1118961742
  • Dimensions ‏ : ‎ 18.29 x 1.78 x 23.11 cm
  • Customer Reviews:
    4.3 4.3 out of 5 stars 33 ratings

About the author

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Michael Bowles

Michael Bowles

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Dr. Michael Bowles (Mike) holds bachelor's and master's degrees in mechanical engineering, an ScD in instrumentation and an MBA. He has worked in academia, technology and business. Mike currently works with startup companies where machine learning is integral to success. He serves variously as part of the management team, a consultant or advisor. He also teaches machine learning courses at Hacker Dojo, a co-working space and startup incubator Mountain View, CA.

Mike was born in Oklahoma and took his bachelor's and master's degrees there, then went to Cambridge for ScD and C. Stark Draper Chair at MIT after graduation. Mike left Boston to work on communications satellites at Hughes Aircraft company in Southern California and then after completing an MBA at UCLA moved to the San Francisco bay area to take roles as founder and CEO of two successful venture-backed startups.

Mike remains actively involved in technical and startup-related work. Recent projects include the use of machine learning in automated trading, predicting biological outcomes on the basis of genetic information, natural language processing for website optimization, predicting patient outcomes from demograpic and lab data and due diligence work on companies in the machine learning and big data arena. Mike can be reached through mbowles.com.

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Trading Central
5.0 out of 5 stars Remains Still Best Practitioners Guide to Key Machine Learning Techniques
Reviewed in Canada on 25 July 2017
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This is a hidden gem amongst the growing number of books now entering the public domain about machine learning. I have had this book for over two years now and it remains my go to book to refresh or simply break a mental log jam. The clarity of explanation remains simply the best for the topic areas covered. While dated in regard to the continuing Python language upgrades, the scripts still work with minimal change to the code. What still remains the best aspect of this volume is the economy of words used to explain very sophisticated machine learning techniques. This remains the best practitioners guide written for the practitioner of machine learning.
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Hari Narayanan P
5.0 out of 5 stars It's a good book to get a decent overview of machine learning ...
Reviewed in India on 27 October 2017
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It's a good book to get a decent overview of machine learning concepts to start working on data science problems
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Ddub
5.0 out of 5 stars Great book on both the fundamentals and getting started!
Reviewed in the United States on 8 May 2015
Verified Purchase
I'm somewhat new to the field, working on a new venture involving predictive analytics. Of the resources I'm using, I keep coming back to this book - the background, problem setup, explanation of tradeoffs, and code examples have been really good. I'm no Python developer, but I was able to narrow down to an algorithm and get it running with Anaconda and Scikit-Learn without much trouble. Compared to a lot of other resources I've seen, this is a great book for a complete view of the recent algorithms along with a how-to on getting started.
17 people found this helpful
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Abhinav
4.0 out of 5 stars Four Stars
Reviewed in India on 1 October 2017
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Nice book but I wouldn't recommend it to beginners.
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Evan
4.0 out of 5 stars Like other reviewers
Reviewed in the United States on 28 October 2015
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Like other reviewers, agree that the discussion of machine learning is very useful, but the code is underwhelming. What I would like to see is a second edition called "Machine Learning in Idiomatic Python", the way I would try to write it.
One person found this helpful
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