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Building Machine Learning Systems with Python
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Expand your Python knowledge and learn all about machine-learning libraries in this user-friendly manual. ML is the next big breakthrough in technology and this book will give you the head-start you need.
Key Features:
- Master Machine Learning using a broad set of Python libraries and start building your own Python-based ML systems
- Covers classification, regression, feature engineering, and much more guided by practical examples
- A scenario-based tutorial to get into the right mind-set of a machine learner (data exploration) and successfully implement this in your new or existing projects
Book Description:
Machine learning, the field of building systems that learn from data, is exploding on the Web and elsewhere. Python is a wonderful language in which to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation and an increasing number of machine learning libraries are developed for Python.Building Machine Learning system with Python shows you exactly how to find patterns through raw data. The book starts by brushing up on your Python ML knowledge and introducing libraries, and then moves on to more serious projects on datasets, Modelling, Recommendations, improving recommendations through examples and sailing through sound and image processing in detail. Using open-source tools and libraries, readers will learn how to apply methods to text, images, and sounds. You will also learn how to evaluate, compare, and choose machine learning techniques. Written for Python programmers, Building Machine Learning Systems with Python teaches you how to use open-source libraries to solve real problems with machine learning. The book is based on real-world examples that the user can build on.
Readers will learn how to write programs that classify the quality of StackOverflow answers or whether a music file is Jazz or Metal. They will learn regression, which is demonstrated on how to recommend movies to users. Advanced topics such as topic modeling (finding a text's most important topics), basket analysis, and cloud computing are covered as well as many other interesting aspects.Building Machine Learning Systems with Python will give you the tools and understanding required to build your own systems, which are tailored to solve your problems.
What You Will Learn:
- Build a classification system that can be applied to text, images, or sounds
- Use scikit-learn, a Python open-source library for machine learning
- Explore the mahotas library for image processing and computer vision
- Build a topic model of the whole of Wikipedia
- Get to grips with recommendations using the basket analysis
- Use the Jug package for data analysis
- Employ Amazon Web Services to run analyses on the cloud
- Recommend products to users based on past purchases
- ISBN-101782161406
- ISBN-13978-1782161400
- PublisherPackt Publishing
- Publication dateJuly 26, 2013
- LanguageEnglish
- Dimensions7.5 x 0.66 x 9.25 inches
- Print length271 pages
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About the Author
Luis Pedro Coelho is a computational biologist who analyzes DNA from microbial communities to characterize their behavior. He has also worked extensively in bioimage informatics - the application of machine learning techniques for the analysis of images of biological specimens. His main focus is on the processing and integration of large-scale datasets. He has a PhD from Carnegie Mellon University and has authored several scientific publications. In 2004, he began developing in Python and has contributed to several open source libraries. He is currently a faculty member at Fudan University in Shanghai.
Product details
- Publisher : Packt Publishing
- Publication date : July 26, 2013
- Language : English
- Print length : 271 pages
- ISBN-10 : 1782161406
- ISBN-13 : 978-1782161400
- Item Weight : 1.15 pounds
- Dimensions : 7.5 x 0.66 x 9.25 inches
- Best Sellers Rank: #2,760,050 in Books (See Top 100 in Books)
- #2,231 in Python Programming
- #7,842 in AI & Machine Learning
- #14,109 in Computer Science (Books)
- Customer Reviews:
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Top reviews from the United States
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- Reviewed in the United States on January 5, 2014Format: KindleVerified PurchaseI 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
- Reviewed in the United States on July 7, 2014Format: PaperbackVerified PurchaseBoth 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.
- Reviewed in the United States on November 4, 2013Format: PaperbackVerified PurchaseThis 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.
- Reviewed in the United States on March 15, 2015Format: KindleVerified PurchaseI 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.
- Reviewed in the United States on August 4, 2014Format: KindleVerified PurchaseI highly recommend this book to anyone who has programmed in Python and are looking to take it to the next level!
- Reviewed in the United States on March 20, 2014Format: KindleVerified PurchaseUnfortunately 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.
- Reviewed in the United States on February 14, 2015Format: KindleVerified PurchaseThere 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"
- Reviewed in the United States on July 20, 2014Format: PaperbackVerified PurchaseOne of the best book in that field I have never read.
Top reviews from other countries
Di CAMILLO JIMReviewed in France on February 29, 20241.0 out of 5 stars Old book :-(
Format: PaperbackVerified PurchaseOld book 2013 version
Old book 2013 version1.0 out of 5 stars
Di CAMILLO JIMOld book :-(
Reviewed in France on February 29, 2024
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SRCReviewed in India on February 11, 20165.0 out of 5 stars Excellent self-learning book
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
Zorba the GreekReviewed in India on March 16, 20184.0 out of 5 stars Not for begineers.
Format: PaperbackVerified PurchaseGood one. Not for begineers. It does not teach ml it teaches the programming.
Anwesh MishraReviewed in India on September 7, 20155.0 out of 5 stars Five Stars
awesome book
ManasaReviewed in India on September 13, 20172.0 out of 5 stars Two Stars
Format: PaperbackVerified PurchaseNot a good book for beginners.
Not explained code in details we will get lot of confusions.





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