Building Machine Learning Systems with Python

by Willi Richert (Author), Luis Pedro Coelho (Author)

48 ratings

ISBN-13: 978-1782161400

ISBN-10: 1782161406

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Building Machine Learning Systems with Python: Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow, 3rd Edition
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As the Big Data explosion continues at an almost incomprehensible rate, being able to understand and process it becomes even more challenging. With Building Machine Learning Systems with Python, you'll learn everything you need to tackle the modern data deluge - by harnessing the unique capabilities of Python and its extensive range of numerical and scientific libraries, you will be able to create complex algorithms that can 'learn' from data, allowing you to uncover patterns, make predictions, and gain a more in-depth understanding of your data.

Featuring a wealth of real-world examples, this book provides gives you with an accessible route into Python machine learning. Learn the Iris dataset, find out how to build complex classifiers, and get to grips with clustering through practical examples that deliver complex ideas with clarity. Dig deeper into machine learning, and discover guidance on classification and regression, with practical machine learning projects outlining effective strategies for sentiment analysis and basket analysis. The book also takes you through the latest in computer vision, demonstrating how image processing can be used for pattern recognition, as well as showing you how to get a clearer picture of your data and trends by using dimensionality reduction.

Keep up to speed with one of the most exciting trends to emerge from the world of data science and dig deeper into your data with Python with this unique data science tutorial.

Learn how to create machine learning algorithms using the flexibility of Python
Get to grips with scikit-learn and other Python scientific libraries that support machine learning projects
Employ computer vision using mahotas for image processing that will help you uncover patterns and trends in your data
Learn topic modelling and build a topic model for Wikipedia
Analyze Twitter data using sentiment analysis
Get to grips with classification and regression with real-world examples

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Editorial Reviews

About the Author

Willi Richert has a PhD in machine learning/robotics, where he has used reinforcement learning, hidden Markov models, and Bayesian networks to let heterogeneous robots learn by imitation. Now at Microsoft, he is involved in various machine learning areas, such as deep learning, active learning, or statistical machine translation. Willi started as a child with BASIC on his Commodore 128. Later, he discovered Turbo Pascal, then Java, then C++ - only to finally arrive at his true love: Python.

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Product details

Publisher ‏ : ‎ Packt Publishing (July 26, 2013)
Language ‏ : ‎ English
Paperback ‏ : ‎ 290 pages
ISBN-10 ‏ : ‎ 1782161406
ISBN-13 ‏ : ‎ 978-1782161400
Item Weight ‏ : ‎ 1.11 pounds
Dimensions ‏ : ‎ 7.5 x 0.66 x 9.25 inches

Best Sellers Rank: #2,134,930 in Books (See Top 100 in Books)
- #2,356 in Python Programming
- #4,079 in AI & Machine Learning
- #5,588 in Computer Programming Languages

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48 ratings

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5 star		42%
4 star		32%
3 star		15%
2 star		11%
1 star 0% (0%)		0%

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Top reviews from the United States

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Subhrajit Bhattacharya

Hands on approach to ML using Python. Thats great - but thats where the greatness ends

Reviewed in the United States on March 15, 2015

Verified Purchase

I can not understand how so many people have given this book a 4 or 5 rating. 3 is the max that can be given.

This book has a hands on approach to ML using python. Which is great. But thats where the greatness ends.

1. The explanations in the book in general are sketchy and not thorough.

2. They use code snippets in the book. Complete code has to be downloaded. However the way the code snippets are explained
does little to help understand the complete code. It's better to read the complete code directly.

3. The code is not portable. I had to make some modifications to make some of the routines work.

4. In some cases I just could not make the code work.

5. They do not explain the scikit and numpy routines at a decent level of detail. So you are often left wondering how the code works,
and then you have to read the scikit or numpy routine to figure this out.

6. There are several perl files for a given chapter. I have no idea in which sequence to go through them.

If you are a decent python programmer, and you have decent knowledge of numpy and scikits, then you will probably not find the book very frustrating. Otherwise, be ready to be frustrated.

I think the reason this book is still selling is its cheap, and i think there is nothing better available.

There a few books which directly discus scikits-learn. Maybe people should look at them.

3 people found this helpful

Helpful

Schwallie

Too Many Bugs in Code

Reviewed in the United States on March 20, 2014

Verified Purchase

Unfortunately this book is basically a non-starter. I can't seem to get through any of it as the code doesn't work in most cases. I agree with a previous reviewer -- I could spend my time debugging and making it work, but that's not why I bought the book. I shouldn't have to jump through hurdles to read this.

I wish I could get a refund.

19 people found this helpful

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J_Stout

Good Learning Tool

Reviewed in the United States on November 4, 2013

Verified Purchase

This book is a good addition to other theoretical books. Although much of the information could be gained through websites, this is a more condensed and straightforward way to learn it. This is not an all in one book and does rely on the reader to fill in the gaps with theoretical knowledge and python programming knowledge. It is well written and easy to follow.

2 people found this helpful

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John

Perfect for the python expert

Reviewed in the United States on February 16, 2018

Verified Purchase

Dives right in. Perfect for the python expert, not so good for a beginner python programmer.

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Brian Du Preez

Quite Frustrating... but a helpful author.

Reviewed in the United States on October 6, 2013

Verified Purchase

Edit:
Willi Richert, has been quite helpful and has looked at the issues I was having and resolved some of them, so especially if you are working on Windows, make sure you get the code from GitHub.
I have not returned to complete working through the rest book as yet, will as soon as I have time.

Original:
To be completely honest I had great hope for this book, it was theoretically exactly what I was looking for, a practical guide to getting up and running with Machine Learning and some of it major Python packages.
But...
From chapter 3, there were code discrepancies between what was in the book, what was supplied and then eventually what I got working...
I am not going to bother going into all the errors / issues, the 2 major ones that made me "shelve" the book and start looking for new study material:
1. After the 9GB download for chapter 5, the supplied source doesn't work and contains requirements to 32bit libs... huge waste of time...
2. After moving onto in chapter 6, and after 24 hours of downloading tweets for sentiment analysis... I checked the files and they only contained "The Twitter REST API v1 is no longer active. Please migrate to API v1.1".

Yes, I could go debug and fix the code / errors in other peoples code... but that is not how I want to spend my time learning a new subject, I have enough of that in my day job as a software developer :)

65 people found this helpful

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Kindle Customer

but seems like it could use more editing and a bit more ...

Reviewed in the United States on February 14, 2015

Verified Purchase

There aren't a lot of reasonably priced intermediate/advanced books on this topic. It's a strong book, but seems like it could use more editing and a bit more polish to make it a great book. I'd still recommend it, but a better book is "Python for Data Analysis"

Helpful

Dr. Howard B. Bandy

Practical advice with useful examples

Reviewed in the United States on July 7, 2014

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Both authors earned PhD degrees related to machine learning, and both are employed applying machine learning to real world problems.

The book illustrates many useful machine learning techniques with well thought out explanations.

One person found this helpful

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Amazon Customer

Practical, clear code examples.

Reviewed in the United States on January 5, 2014

Verified Purchase

I love this book. It provides a lot of practical, clear examples and explanations that a lot of other machine learning courses just don't provide or are too slow to reach (for my ADD). I love the way the author gives enough explanation for you to grasp the concepts involved with practical machine learning systems, without going into so much detail that you just give up halfway through. 5 stars, I'm definitely looking forward to more books of this quality from Willi Richert.

Daniel

One person found this helpful

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Top reviews from other countries

SRC

Excellent self-learning book

Reviewed in India on February 11, 2016

Verified Purchase

Excellent book ... simple and structured introduction to python and data science using python ... plus well worked out examples ... one of the few self-learning books available on the market

One person found this helpful

Manasa

Two Stars

Reviewed in India on September 13, 2017

Verified Purchase

Not a good book for beginners.
Not explained code in details we will get lot of confusions.

One person found this helpful

Zorba the Greek

Not for begineers.

Reviewed in India on March 16, 2018

Verified Purchase

Good one. Not for begineers. It does not teach ml it teaches the programming.

One person found this helpful

Mustafa Vadnagarwala

Four Stars

Reviewed in India on January 18, 2017

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Anwesh Mishra

Five Stars

Reviewed in India on September 7, 2015

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awesome book

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Building Machine Learning Systems with Python

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