Machine Learning In Python W/Ws 1st Edition

by Michael Bowles (Author)

27 ratings

ISBN-13: 978-1118961742

ISBN-10: 1118961749

Why is ISBN important?

Paperback

$14.99

Other Sellers

from

Other Sellers

See all 1 versions

Used: Good | Details

Sold by Dog-Eared Books NC

Access codes and supplements are not guaranteed with used items.

$3.99 delivery May 26 - June 1. Details

Select delivery location

There is a newer edition of this item:

Machine Learning with Spark and Python: Essential Techniques for Predictive Analytics
$18.00
(4)
Only 4 left in stock - order soon.

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.

Willi Richert
48
Paperback
14 offers from $13.15

Editorial Reviews

From the Back Cover

SIMPLE, EFFECTIVE WAY TO ANALYZE DATA AND PREDICT OUTCOMES WITH PYTHON

Machine learning focuses on prediction―using what you know to predict what you would like to know based on historical relationships between the two. At its core, it's a mathematical/algorithm-based technology that, until recently, required a deep understanding of math and statistical concepts, and fluency in R and other specialized languages. Machine Learning in Python simplifies machine learning for a broader audience and wider application by focusing on two algorithm families that effectively predict outcomes, and by showing you how to apply them using the popular and accessible Python programming language.

Author Michael Bowles draws from years of machine learning expertise to walk you through the design, construction, and implementation of your own machine learning solutions. The algorithms are explained in simple terms with no complex math, and sample code is provided to help you get started right away. You'll delve deep into the mechanisms behind the constructs, and learn how to select and apply the algorithm that will best solve the problem at hand, whether simple or complex. Detailed examples illustrate the machinery with specific, hackable code, and descriptive coverage of linear regression and ensemble methods helps you understand the fundamental processes at work in machine learning. The methods are effective and well tested, and the results speak for themselves.

Designed specifically for those without a specialized math or statistics background, Machine Learning in Python shows you how to:

Select the right algorithm for the job
Learn the mechanisms and prepare the data
Master core Python machine learning packages
Build versatile predictive models that work
Apply trained models in practice for various uses
Measure model performance for better QC and application
Use provided sample code to design and build your own model

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 (April 17, 2015)
Language ‏ : ‎ English
Paperback ‏ : ‎ 360 pages
ISBN-10 ‏ : ‎ 1118961749
ISBN-13 ‏ : ‎ 978-1118961742
Item Weight ‏ : ‎ 1.36 pounds
Dimensions ‏ : ‎ 7.2 x 0.7 x 9.1 inches

Best Sellers Rank: #2,258,294 in Books (See Top 100 in Books)
- #534 in Data Warehousing (Books)
- #618 in Machine Theory (Books)
- #1,102 in Artificial Intelligence (Books)

Customer Reviews:
27 ratings

About the author

Follow authors to get new release updates, plus improved recommendations.

Michael Bowles

Brief content visible, double tap to read full content.

Full content visible, double tap to read brief content.

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.

Customer reviews

5 star		47%
4 star		22%
3 star		24%
2 star		7%
1 star 0% (0%)		0%

How customer reviews and ratings work

Top reviews from the United States

There was a problem filtering reviews right now. Please try again later.

RHV

Code implemented is poor. Explanation of algorithms is good.

Reviewed in the United States on August 24, 2015

Verified Purchase

I have no idea why the author didn't implement pandas or numpy instead of devoting pages to reading csv files with base python. He chose to store data in lists of lists. Why??? He chose to normalize and store data "the ol' fashioned way" instead of using numpy arrays. This makes the code less readable. (Seriously, every example begins with several lines of code simply to read the data, loop through each line, split on a ",", append to a list, etc. Using pandas' read_csv or numpy's genfromtxt code have accomplished the same thing in one line!)

The author does say, however, that he included a lot of code to help explain how the algorithms work. This is actually helpful--when it relates specifically to a machine learning algorithm. He subsequently makes use of scikit-learn objects in the examples, as he should.Why he didn't follow the same approach when reading CSV files, I have no idea.

My recommendation is as follows. If you're looking for code to implement, I would not use this book for proper examples. If you're looking for an explanation of penalized regression and ensemble algorithms, this book is pretty good for that purpose in my opinion.

31 people found this helpful

Helpful

M.L.

Just what we were looking for

Reviewed in the United States on September 15, 2015

Verified Purchase

Got this for a colleague who is implementing analytics in our product. The book addresses the topic clearly, is well structured in terms of order and flow, and covers the topics in the exact level of detail that is promised in the description. It's a great introduction with enough depth to get you started.

3 people found this helpful

Helpful

Evan

Like other reviewers

Reviewed in the United States on October 28, 2015

Verified Purchase

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

Helpful

Scott Steele

Great Practical Book

Reviewed in the United States on May 30, 2015

Verified Purchase

Before reading this, I think you've got to have some fundamental knowledge of statistics, data science, and Python. It's a great practical book for supervised learning applications in that it narrows things down into actionable information; you can easily insert your datasets into the example algorithms and get results.

A good book on data science fundamentals is "Data Science for Business," Provost & Fawcett

8 people found this helpful

Helpful

Hugh D. Brown

Great text, crummy code

Reviewed in the United States on October 18, 2015

Verified Purchase

The text is great but the code is horrific -- repetitive, fails to use common libraries like numpy and pandas which would greatly simplify examples. Buy for the text, ignore the code.

2 people found this helpful

Helpful

Phil C.

Some very useful concepts but...

Reviewed in the United States on August 6, 2015

Verified Purchase

Some very useful concepts but lots, and I mean lots, of duplicated code. Also, the basic premise of two fit all algorithm families may work for a quick start but is not a complete picture. Overall, it is useful but imperfect.

2 people found this helpful

Helpful

Ddub

Great book on both the fundamentals and getting started!

Reviewed in the United States on May 8, 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

Helpful

resimatic

Five Stars

Reviewed in the United States on December 22, 2015

Verified Purchase

A good book on the subject, not just for Python, but good coverage of the concepts in practical terms.

2 people found this helpful

Helpful

Top reviews from other countries

Trading Central

Remains Still Best Practitioners Guide to Key Machine Learning Techniques

Reviewed in Canada on July 25, 2017

Verified Purchase

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.

See all reviews

Machine Learning In Python W/Ws 1st Edition

There is a newer edition of this item:

Customers who bought this item also bought

Editorial Reviews

From the Back Cover

About the Author

Product details

About the author

Michael Bowles

Customer reviews

Top reviews from the United States

There was a problem filtering reviews right now. Please try again later.

Top reviews from other countries