Building Machine Learning Systems with Python

Expand your Python knowledge and learn all about machine-learning libraries in this user-friendly manual. ML is the next big breakthrough in technology and this book will give you the head-start you need.

Key Features:

• Master Machine Learning using a broad set of Python libraries and start building your own Python-based ML systems
• Covers classification, regression, feature engineering, and much more guided by practical examples
• A scenario-based tutorial to get into the right mind-set of a machine learner (data exploration) and successfully implement this in your new or existing projects

Book Description:

Machine learning, the field of building systems that learn from data, is exploding on the Web and elsewhere. Python is a wonderful language in which to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation and an increasing number of machine learning libraries are developed for Python.Building Machine Learning system with Python shows you exactly how to find patterns through raw data. The book starts by brushing up on your Python ML knowledge and introducing libraries, and then moves on to more serious projects on datasets, Modelling, Recommendations, improving recommendations through examples and sailing through sound and image processing in detail. Using open-source tools and libraries, readers will learn how to apply methods to text, images, and sounds. You will also learn how to evaluate, compare, and choose machine learning techniques. Written for Python programmers, Building Machine Learning Systems with Python teaches you how to use open-source libraries to solve real problems with machine learning. The book is based on real-world examples that the user can build on.

Readers will learn how to write programs that classify the quality of StackOverflow answers or whether a music file is Jazz or Metal. They will learn regression, which is demonstrated on how to recommend movies to users. Advanced topics such as topic modeling (finding a text's most important topics), basket analysis, and cloud computing are covered as well as many other interesting aspects.Building Machine Learning Systems with Python will give you the tools and understanding required to build your own systems, which are tailored to solve your problems.

What You Will Learn:

• Build a classification system that can be applied to text, images, or sounds
• Use scikit-learn, a Python open-source library for machine learning
• Explore the mahotas library for image processing and computer vision
• Build a topic model of the whole of Wikipedia
• Get to grips with recommendations using the basket analysis
• Use the Jug package for data analysis
• Employ Amazon Web Services to run analyses on the cloud
• Recommend products to users based on past purchases

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 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.