Building Machine Learning Systems with Python - Second Edition

More Information
Learn
  • Build a classification system that can be applied to text, images, or sounds
  • Use NumPy, SciPy, scikit-learn – 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
About

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.

Features
  • 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
Page Count 326
Course Length 9 hours 46 minutes
ISBN 9781784392772
Date Of Publication 26 Mar 2015

Authors

Willi Richert

Willi Richert has a PhD in machine learning/robotics, where he has used reinforcement learning, hidden Markov models, and Bayesian networks to let heterogeneous robots learn by imitation. Now at Microsoft, he is involved in various machine learning areas, such as deep learning, active learning, or statistical machine translation. Willi started as a child with BASIC on his Commodore 128. Later, he discovered Turbo Pascal, then Java, then C++ - only to finally arrive at his true love: Python.

Luis Pedro Coelho

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.