Building Machine Learning Systems with Python - Second Edition: Get more from your data through creating practical machine learning systems with Pytho (Paperback or Softback)
Language: English
Published by Packt Publishing 3/26/2015, 2015
- Softcover
- New
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Building Machine Learning Systems with Python - Second Edition: Get more from your data through creating practical machine learning systems with Pytho.
Seller Inventory # BBS-9781784392772
- Title
- Building Machine Learning Systems with Python - Second Edition: Get more from your data through creating practical machine learning systems with Pytho (Paperback or Softback)
- Author
- Richert, Willi
- Publisher
- Packt Publishing 3/26/2015
- Publication year
- 2015
- Condition
- New
- Book Type
- Book
- Binding
- Paperback or Softback
- Language
- English
- ISBN 10
- 1784392774
- ISBN 13
- 9781784392772
- Edition
- 2nd Edition
- Item weight
- 1.24 pounds
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.
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.
"Synopsis" may belong to another edition of this title.
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.
"About the title" may belong to another edition of this title.
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