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Building Machine Learning Systems With Python - Second Edition

Get more from your data through creating practical machine learning systems with Python About This BookBuild your own Python-based machine learning systems tailored to solve any problemDiscover how Python offers a multiple context solution for create machine learning systemsPractical scenarios using the key Python machine learning libraries to successfully implement in your projectsWho This Book Is ForThis book primarily targets Python developers who want to learn and use Python's machine learning capabilities and gain valuable insights from data to develop effective solutions for business problems.What You Will Learn Build a classification system that can be applied to text, images, or sounds Use NumPy, SciPy, scikit-learn a€“ scientific Python open source libraries for scientific computing and machine learning Explore the mahotas library for image processing and computer vision Build a topic model for the whole of Wikipedia Employ Web Services to run analysis on the cloud Debug machine learning problems Get to grips with recommendations using basket analysis Recommend products to users based on past purchases In DetailUsing machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python is a wonderful language to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation. With its excellent collection of open source machine learning libraries you can focus on the task at hand while being able to quickly try out many ideas.This book shows you exactly how to find patterns in your raw data. You will start by brushing up on your Python machine learning knowledge and introducing libraries. You'll quickly get to grips with serious, real-world projects on datasets, using modeling, creating recommendation systems. Later on, the book covers advanced topics such as topic modeling, basket analysis, and cloud computing. These will extend your abilities and enable you to create large complex systems.With this book, you gain the tools and understanding required to build your own systems, tailored to solve your real-world data analysis problems.

Paperback: 305 pages

Publisher: Packt Publishing - ebooks Account; 2 edition (March 31, 2015)

Language: English

ISBN-10: 1784392774

ISBN-13: 978-1784392772

Product Dimensions: 7.5 x 0.7 x 9.2 inches

Shipping Weight: 1.6 pounds (View shipping rates and policies)

Average Customer Review: 3.2 out of 5 stars  See all reviews (5 customer reviews)

Best Sellers Rank: #364,966 in Books (See Top 100 in Books) #59 in Books > Computers & Technology > Web Development & Design > User Generated Content #262 in Books > Computers & Technology > Databases & Big Data > Data Processing #321 in Books > Computers & Technology > Programming > Languages & Tools > Python

Wishing to learn Python's machine-learning toolkit - I am an emigrant from R Country - I rounded up several relevant books, and set out to narrow the field to one or two suitable for further study. My haul included (in no particular order)"Machine Learning in Python" by Bowles, published in 2015 by Wiley, 360 pages, $25 for the cheapest hardcopy now available from (including shipping)"Designing Machine Learning Systems with Python" by Julian, 2016, Packt, 232 pages, $42"Mastering Python for Data Science" by Madhavan, 2015, Packt, 294 pages, $39"Learning Data Mining with Python" by Layton, 2015, 369 pages, $43"Python Data Science Cookbook" by Subramanian, 2015, 347 pages, $48"Data Science From Scratch" by Grus, 2015, 330 pages, $24"Learning scikit-learn" by Moncecchi and Garreta, 2013, 118 pages, $28"Building Machine Learning Systems with Python" by Coelho and Richert, 2015, 305 pages, $49"Python Machine Learning" by Raschka, 2015, 454 pages, $34The whittling-down turned out to be harder than expected: Python titles are better than R counterparts, and Madhavan's book alone was easy to dismiss. Subramanian, Moncecchi-Garreta and Julian did not make the cut based on comparison with alternatives, but weren't terrible. Grus is the beginner's best bet - beginners can stop reading here - while Bowles is a book which I like a lot, but which may be a bit too specialist. As a reviewer, thinking about what other "intermediate" readers might find useful, I end up pointing to the trio of Raschka, Layton and Coelho-Richert as the books worth choosing from.

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