Machine Learning in Action 1st Edition

by Peter Harrington (Author)

67 ratings

ISBN-13: 978-1617290183

ISBN-10: 9781617290183

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Summary

Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.
About the Book
A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many.

Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification.

Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful.

Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book.
What's Inside

A no-nonsense introduction
Examples showing common ML tasks
Everyday data analysis
Implementing classic algorithms like Apriori and Adaboos

Table of Contents

PART 1 CLASSIFICATION
Machine learning basics
Classifying with k-Nearest Neighbors
Splitting datasets one feature at a time: decision trees
Classifying with probability theory: naïve Bayes
Logistic regression
Support vector machines
Improving classification with the AdaBoost meta algorithm
PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION
Predicting numeric values: regression
Tree-based regression
PART 3 UNSUPERVISED LEARNING
Grouping unlabeled items using k-means clustering
Association analysis with the Apriori algorithm
Efficiently finding frequent itemsets with FP-growth
PART 4 ADDITIONAL TOOLS
Using principal component analysis to simplify data
Simplifying data with the singular value decomposition
Big data and MapReduce

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Editorial Reviews

About the Author

Peter Harrington holds a Bachelors and a Masters Degrees in Electrical Engineering. He is a professional developer and data scientist. Peter holds five US patents and his work has been published in numerous academic journals.

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Product details

ASIN ‏ : ‎ 1617290181
Publisher ‏ : ‎ Manning; 1st edition (April 19, 2012)
Language ‏ : ‎ English
Paperback ‏ : ‎ 384 pages
ISBN-10 ‏ : ‎ 9781617290183
ISBN-13 ‏ : ‎ 978-1617290183
Item Weight ‏ : ‎ 1.55 pounds
Dimensions ‏ : ‎ 7.38 x 0.8 x 9.25 inches

Customer Reviews:
67 ratings

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TheKars

Great Book: A few things might help

Reviewed in the United States on April 27, 2022

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This book is more details oriented and thorough than the other books I have read so far.
Chapter 1: How do you find K in KNN? is it the square root of the population size?
How do you find errors in KNN? The author has discussed only the accuracy metrics.
Is there any room for cost adjustment (like weighted distance measurement)?
Finally, I believe, KNN has a better application than handwriting identification because that works better with NN.
Chapter 5: While estimating the loss, simply used simple weight differences. A cross-entropy function could have been discussed.

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Atreya

Good attempt but needs LOT of improvement

Reviewed in the United States on June 17, 2012

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Looking at many good reviews on amazon, I decided to purchase this book. It's a decent book, but IMO it has been edited poorly and the code has not been tested properly.

The introduction chapter got me really excited, just like other Manning's "in Action" books do. But once I started executing the code in chapter 2 "Classifying with k-nearest neighbors" I realized that the code had bugs. Though I could figure out what's wrong and fix the bugs, I did not expect this from Manning, after having read some of their excellent books like ( The Quick Python Book, Second Edition , Spring in Action and Hadoop in Action ).

Moreover the book has some introduction to python and numpy in appendix A. I believe the author could have pointed the reader elsewhere for learning python and those pages could have been used to explain more of numpy and matplotlib, which the author uses freely without any explanation in the text. (Yup, be ready to read some online numpy and matplotlib tutorials and documentation.)

If you don't know python, then you can do what I did: read The Quick Python Book, Second Edition and then attempt this book.

The figures in the book are not in color so you need to execute the code to understand what the author is telling. It forces you to actually run the code, which is good, but you can't read this book without a computer in front of you.

Finally, I am a big believer in following the conventions of a language. I would have been really happy had the author followed PEP8 ([...]), because along with learning machine learning, you could have learnt some good python coding practices.

36 people found this helpful

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anonymous

College / enthusiastic high school students are appropriate audience

Reviewed in the United States on April 24, 2017

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It is an OK book to skim and get a general idea on different topics. He states in the introduction that he wanted to write a book that bridge the gap between thinkers and doers. This book does not do this, it is falls decidedly on the "doing" part of the spectrum.

Pros:
1. Conversational tone, easy to comprehend.
2. Python examples (I have not tried them)
3. Concise description of algorithms. Gives basic insight, but not much else.
4. If new, you get a free e-book copy.

Cons:
1. Lack of rigor.
a) little theory or mathematics.
b) p40, wikipedia citation (really?)
2. There is a decent amount of overhead to teaching Python (which you can skip if you already know Python). In my opinion, it is wasted space because this isn't a programming book and you won't actually learn Python.
3. Very example heavy.

The appropriate audience for this book is probably college freshman (or possibly enthusiastic high school level). For postgraduates, this book is a disappointment.

3 people found this helpful

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enty

Good Overview of Machine Learning

Reviewed in the United States on December 1, 2012

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This book is a good introduction to the main algorithms used in machine learning: linear/logistics regression, kNN, decision and regression trees, naive Bayes, support vector machines, AdaBoost, SVD, and PCA. The author does a good job as presenting complex concepts in a simple fashion.
However, many chapters feel more like simplistic summaries around Python code and the editing can be poor at times repeating the same information between a main paragraph and a shaded summary on the next page. I would still recommend this book as a fairly broad overview of these techniques and a valuable starting point for implementing them. It is easy to read and offers a good selection of algorithms.
If you are looking for a more formal alternative, I can only recommend the book from Hastie, Tibshirani, and Friedman: The Elements of Statistical Learning . That book presents a more rigorous approach to the same algorithms, their goals, limitations and main variants. It is presented as a reference book but does not drown the reader in an sea of formulas, unlike similar reference books.

3 people found this helpful

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Caihao Cui

Great Book for machine learning and Python lover

Reviewed in the United States on March 5, 2015

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Great Book for machine learning and Python lover, I am sure it is a great book for people who are not familiar with matlab, this may be the fast way to get yourself to do some really work rather than keep reading a lot of papers or pseudo codes.
It is not very difficult to read and practice, sometimes you may think of some better ideas from the book.
Also the idea of machine learning methods could also be helpful when you use other programming language to do some designs.

3 people found this helpful

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Ruoshi Li

Good book for code practice

Reviewed in the United States on November 21, 2019

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Good book if you want to practice writing code for some ML algorithms. Not so great for explaining all the mathematical theory/derivation steps. Use it along with CS 229 notes.

One person found this helpful

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Ken L

Great book for getting into machine learning

Reviewed in the United States on July 4, 2012

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This is a very good way to quickly and easily explore machine learning techniques. The code snippets are generally well explained, and short enough to be digestible. Being able to play around with the data and algorithms in an interactive way really helps the concepts sink in. I'm actually reading the book on my Kindle (the printed version includes a code to download an electronic version), and on my Kindle the book includes color figures and useful hyperlinks. As other reviewers have pointed out, some of the code examples have errors, which is frustrating. The book's website has an errors and corrections forum topic, but it's a pain to have to go there when the code does not work. However, the good (and there is much goodness) greatly outweighs the bad. If you like a hands-on approach to learning ML techniques (and don't mind chasing a few bugs) then you will love this book.

7 people found this helpful

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Top reviews from other countries

Amrit Dutta

Not a must read, good to read to scan the whole ML landscape.

Reviewed in India on June 9, 2018

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Cons : This book has multiple targets - ML, MATH, PYTHON , thus failed to provide clear understanding of any one, Prior to reading this book i had good understanding of math , thus i can clearly tell nobody will understand the core mathematical concepts involved in the algos.

Pros: If you are starting afresh , then this book can tell you what you have to learn for ML. It has provided good analysis to dissect the landscape of ML.

One person found this helpful

Deepanchakkaravarthy

Worst paper quality for this price

Reviewed in India on January 18, 2022

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Not even used white paper for print. Use muddy paper. Worst quality I ever seen books. You can see the quality of printing text in picture. The book looks like xerox copy taken from original book i think so.

Subhadeep Roy

Duplicate. Poor print. Missing Pages in between

Reviewed in India on June 19, 2016

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This review is NOT regarding the book contents. Please be aware that this is a duplicate copy and looks more like a xerox of the original. I understand that this is a trade-off for the price but the print quality is very poor. Diagrams cannot be clearly read and there are lots of missing pages. There is not point in buying such a book in low price if the print quality is unreadable in most of the places.

6 people found this helpful

Alan

Good Book for Those Starting Machine Learning.

Reviewed in India on November 6, 2018

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The book is useful for those starting machine learning although the codes have not been updated to the latest python version so you may find looking up the latest alternatives for some codes.

Geevarghese samuel

Must buy for anyone wanting to take a deep dive into Machine Learning

Reviewed in India on February 2, 2017

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A really good book that introduces ML algorithms. Many common ML algorithms are introduced and implemented. The book may appear a bit complex for someone who just started machine learning. Mix the contents of this book with some good courses online, and you are good to go.

The book is not for the impatient or faint hearts. The book shows actual implementation of various ML algorithms in Python using NumPy library.

2 people found this helpful

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Machine Learning in Action 1st Edition

PART 1 CLASSIFICATION

PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION

PART 3 UNSUPERVISED LEARNING

PART 4 ADDITIONAL TOOLS

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