Building Machine Learning Systems with Python - Second Edition: Get more from your data through creating practical machine learning systems with Python (Paperback)

Get more from your data with the power of Python machine learning systems

Key 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

Book Description:

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.

What You Will 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

Who this book is for: