Building Machine Learning Systems with Python Author: Willi Richert | Language: English | ISBN:
1782161406 | Format: EPUB
Building Machine Learning Systems with Python Description
About the Author
Willi Richert
Willi Richert has a PhD in Machine Learning/Robotics and currently works for Microsoft in the Bing Core Relevance Team. He performs statistical machine translation.
Luis Pedro Coelho
Luis Pedro Coelho is a Computational Biologist: someone who uses computers as a tool to understand biological systems. Within this large field, Luis works in Bioimage Informatics, which is the application of machine learning techniques to the analysis of images of biological specimens. His main focus is on the processing of large scale image data. With robotic microscopes, it is possible to acquire hundreds of thousands of images in a day, and visual inspection of all the images becomes impossible. Luis has a PhD from Carnegie Mellon University, which is one of the leading universities in the world in the area of machine learning. He is also the author of several scientific publications. Luis started developing open source software in 1998 as a way to apply to real code 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 mahotas, the popular computer vision package for Python, and is the contributor of several machine learning codes..
- Paperback: 290 pages
- Publisher: Packt Publishing (July 26, 2013)
- Language: English
- ISBN-10: 1782161406
- ISBN-13: 978-1782161400
- Product Dimensions: 9.2 x 7.5 x 0.6 inches
- Shipping Weight: 1.2 pounds (View shipping rates and policies)
Machine learning is an intricate philosophy and it involves lot of mathematical complexities to bring it into a practice of data analysis. This book simply eradicate those intricacies of programming and implementation of machine learning algorithms. In all, it makes machine learning code pretty simple. Understanding "WHAT" is machine learning is not the purpose of this book. However, this book is designed around the concept "HOW" to implement machine learning algorithms. I would like to add here that it is not only explain you "HOW" to program the algorithms but it also helps you to think "HOW BEST" we can program it. Let me start with some + and few - of the books. But before that remember, as title clarifies, this book is all around (hovers around) Python implementation of machine learning i.e. SCIKIT-LEARN libraries, Scipy and NUMPy. That's the boundary.
+
1. Very clear and precise declaration from Author that this book is more about implementation of ML than Concept.
2. It starts with teaching very basic of data analysis of preprocessing and cleaning up the data along with implementation of Array, indexes, Vector and Matrices using python libraries. This helps reader to make aware about WHAT basics they should build before getting into more complex problems of machine learning. I really liked the "tiny" machine learning program. It's like writing "Hello Word" in any other programming book.
3. Beauty is that it takes you slowly into the implementation of classification problem, Text data processing, Clustering, Regression and sentiment analysis.
4. Though the breadth of topics is vast but it touches every small corner of related topic. For example: When explaining text comparison method it explains how STOP WORD can be done?
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