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  • Building Machine Learning Systems with Python - Second Edition

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Willi Richert
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Building Machine Learning Systems with Python - Second Edition 2nd ed. Edition

by Willi Richert (Author), Luis Pedro Coelho (Author)
3.6 out of 5 stars (11)
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This book primarily targets programmers and aspiring data scientists who have already taken their first steps in Python and would want to learn and bring the power of machine learning to their new projects. For those who would like to add machine learning support into their existing projects, this book would be equally beneficial. It's also intended for everyone who would like to learn how to use Python's machine learning capabilities and gain valuable insight from data to develop effective solutions for business problems.
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  1. ISBN-10
    1784392774
  2. ISBN-13
    978-1784392772
  3. Edition
    2nd ed.
  4. Publisher
    Packt Publishing
  5. Publication date
    March 26, 2015
  6. Language
    English
  7. Dimensions
    7.5 x 0.74 x 9.25 inches
  8. Print length
    305 pages
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Editorial Reviews

About the Author

Willi Richert is a Software Development Engineer at Microsoft for the last 5 years. He has also worked as a project team leader for mobile devices (Java/Python) at Richert GbR.

Product details

  • Publisher ‏ : ‎ Packt Publishing
  • Publication date ‏ : ‎ March 26, 2015
  • Edition ‏ : ‎ 2nd ed.
  • Language ‏ : ‎ English
  • Print length ‏ : ‎ 305 pages
  • ISBN-10 ‏ : ‎ 1784392774
  • ISBN-13 ‏ : ‎ 978-1784392772
  • Item Weight ‏ : ‎ 1.3 pounds
  • Dimensions ‏ : ‎ 7.5 x 0.74 x 9.25 inches
  • Customer Reviews:
    3.6 out of 5 stars (11)

About the author

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Willi Richert

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Customer reviews

3.6 out of 5 stars
11 global ratings

Top reviews from the United States

  • Jason Woodcock
    3.0 out of 5 stars Okay. But...
    Reviewed in the United States on August 5, 2017
    Format: PaperbackVerified Purchase
    Not as well written as some of the other Packt publishing books. While it does give some solid examples, the author is severely lacking in the explanations department.
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  • braxen
    3.0 out of 5 stars A reasonable addition to a ML library with good info
    Reviewed in the United States on August 26, 2016
    Format: Paperback
    Following research on Amazon and reviewing the different comments and the contents when available, I purchased a series of books on Machine Learning.

    So far, I have read (Python Machine Learning Paperback – by Sebastian Raschka) which I give a 4 stars, see review.

    Building Machine Learning Systems suffers from code that is a bit elliptic and not as well explained as Raschka's. This may be confusing and leave you wanting for more. Contrary to Rashka's book, Neural Networks are not addressed.

    However, as said by some reviewers, the concepts are interesting and the book includes important topics that are not covered by Raschka.
    In addition, there is a starting chapter on image recognition, big data is introduced as well a cloud computing services from Amazon. Interesting new python libraries are introduced as well.

    Conclusion:
    -Clear code that will get you started fast, don't expect as you will need to research further and find explanations elsewhere.
    +Not a rehash of existing literature of ML and goes beyond the most common concepts - very valuable to up your level and give you access to more sophisticated ML algorithms.
    +Good for a mid-level practitioner beyond the basics of ML, not the "holy graal".

    I'd rank Raschka higher with an easier pratical edge but "Building Machine Learning Systems" is not to be discarded. Cherry pick topics when you have more experience.
    2 people found this helpful
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  • John Malis
    2.0 out of 5 stars Terribly written code
    Reviewed in the United States on May 19, 2015
    Format: Kindle
    I bought this with really high hopes of being the bridge I needed to cross the gap of putting theory into program, and was sorely disappointed. The code examples in this book are so horribly written, that I don't think I can keep going past chapter two. Simple, basic programming conventions such as variable naming are so sloppy that the example code is a mess and nearly or completely unreadable. Program flow doesn't exist as these examples are haphazardly banged out onto the REPL (that's right, the whole book looks like it's just like test code written in IDLE snippets) while arrays are constantly reassigned to hold slices of information that don't reflect, at all, what they were originally created for for added head scratching. What might be the most frustrating thing, however, is that comments are reserved for the most basic, obvious statements while complicated Numpy tricks and seemingly unnecessary reassignment and nested looping happen without a word or pause.

    The authors happily thrash and trash PEP 8 and the result is utterly predictable: unusable, unreadable, and incomprehensible examples. Whatever information this book is trying to get across, I can't read it.
    13 people found this helpful
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  • Eli
    3.0 out of 5 stars Code examples are difficult; but the concepts are good.
    Reviewed in the United States on May 23, 2015
    Format: Paperback
    I will agree with the above poster that the code is somewhat difficult to follow. Then again, given that they use real world data, I can't completely blame them because things can get sloppy when using real world data. The majority of the complex code comes from trying to clean up the data; and some of the code examples would be clearer with a little more guidance on whats going on. However, I do think that the machine learning side of this book is very sound and the authors do a very good job of taking you through practical examples rather than just the typical public data sets most books go through. So code aside, the practical machine learning side gets high marks from me.
    8 people found this helpful
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  • Clifford J. Kasper
    4.0 out of 5 stars Good book and easy read
    Reviewed in the United States on June 3, 2015
    Format: Kindle
    This book was extremely easy to read, something difficult to do considering the topic. I read this book for increased knowledge to apply to my current job and I feel as if that desire has been met. I would recommend it to anyone who wants to increase their Python skills for hobby or employment.
    Additionally, the book provides code examples and things which the user can experiment and learn with as they read the book. You do not need to be in a corporate environment to take applicable knowledge away from this book.
    Great read.
    3 people found this helpful
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Top reviews from other countries

  • Jiannan Zhu
    2.0 out of 5 stars Solid python skills are required, Code snipets are difficult to read/run.
    Reviewed in the United Kingdom on September 4, 2016
    Format: PaperbackVerified Purchase
    OK, first of all, you need to have a solid experience on Python, (ask urself if you are familiar with map, filter, generator, iterator, functools ... luckily I had some experience on python, I use this book merely as a guidance for sklearn), if you dont have any, then you would feel very struggling with reading the code spinets in this book. It simply will not give any explanation. From the bright side, it does push urself to learn more about the python language and some other libraries.

    Secondly, more than half of its examples/code pieces, you need to go to the author's webpage to read his project files. Otherwise, for most of its codes, you will never be able to read/run it. Now im about 50% on reading this book, I tried to reproduce every results and had read through every lines of the author's code. I found at least 3 errors/bugs on its code.

    Also, for people who already brought this book, the following part may save you some time:
    1. the Pyenchant doesnt seem working for window x64 system.
    2. On P103, in the code snipet to calculate X, post_id returned from fetch_posts is 'int', whilst all_answers is a list of 'str'.
    ... other errors, cannot remember.

    Basically, so far from starting this book, I got what I want, apart from some annoying errors/bugs and having to check the author's Github frequently, so I give it a 2-3 stars. But I probably will not recommend it to other people, unless someone who only wants to sharpen his python skills with weird interests in code debugging.

    Apologize for my poor English...
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