Building Machine Learning Systems with Python - Second Edition, by Luis Pedro Coelho, Willi Richert
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Building Machine Learning Systems with Python - Second Edition, by Luis Pedro Coelho, Willi Richert
Best Ebook PDF Building Machine Learning Systems with Python - Second Edition, by Luis Pedro Coelho, Willi Richert
Get more from your data through creating practical machine learning systems with Python
About This Book
- Build your own Python-based machine learning systems tailored to solve any problem
- Discover how Python offers a multiple context solution for create machine learning systems
- Practical scenarios using the key Python machine learning libraries to successfully implement in your projects
Who This Book Is For
This 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 Amazon 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 Detail
Using 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.
Building Machine Learning Systems with Python - Second Edition, by Luis Pedro Coelho, Willi Richert- Amazon Sales Rank: #1272184 in Books
- Published on: 2015-03-31
- Released on: 2015-03-26
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .74" w x 7.50" l, 1.24 pounds
- Binding: Paperback
- 305 pages
About the Author
Luis Pedro Coelho
Luis Pedro Coelho is a computational biologist: someone who uses computers as a tool to understand biological systems. In particular, Luis analyzes DNA from microbial communities to characterize their behavior. Luis 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. Luis has a PhD from Carnegie Mellon University, one of the leading universities in the world in the area of machine learning. He is the author of several scientific publications. Luis started developing open source software in 1998 as a way to apply real code to what he was learning in his computer science courses at the Technical University of Lisbon. In 2004, he started developing in Python and has contributed to several open source libraries in this language. He is the lead developer on the popular computer vision package for Python and mahotas, as well as the contributor of several machine learning codes. Luis currently divides his time between Luxembourg and Heidelberg.
Willi Richert
Willi Richert has a PhD in machine learning/robotics, where he used reinforcement learning, hidden Markov models, and Bayesian networks to let heterogeneous robots learn by imitation. Currently, he works for Microsoft in the Core Relevance Team of Bing, where he is involved in a variety of ML areas such as active learning, statistical machine translation, and growing decision trees.
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Most helpful customer reviews
9 of 10 people found the following review helpful. Terribly written code By John Malis 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.
5 of 6 people found the following review helpful. Code examples are difficult; but the concepts are good. By Eli 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.
3 of 5 people found the following review helpful. Good book and easy read By Clifford J. Kasper 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.
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