Building Machine Learning Systems with Python - Second Edition

Building Machine Learning Systems with Python - Second Edition

by Willi Richert, Luis Pedro Coelho

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Overview

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.

Product Details

ISBN-13: 9781784392888
Publisher: Packt Publishing
Publication date: 03/26/2015
Sold by: Barnes & Noble
Format: NOOK Book
File size: 10 MB

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.

Table of Contents

  1. Getting Started with Python Machine Learning
  2. Classifying with Real-world Examples
  3. Clustering - Finding Related Posts
  4. Topic Modeling
  5. Classification - Detecting Poor Answers
  6. Classification II - Sentiment Analysis
  7. Regression
  8. Recommendations
  9. Classification - Music Genre Classification
  10. Computer Vision
  11. Dimensionality Reduction
  12. Bigger Data

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