Packt is pleased to announce the release of Building Machine Learning System with Python,a hands-on guide to mastering the art of machine learning with Python. The book is out now and available from Packt in print and all the popular eBook formats.
About the Authors:
Willi Richert has a PhD in Machine Learning and Robotics, and he currently works for Microsoft in the Core Relevance Team of Bing, where he is involved in a variety of machine learning areas such as active learning and statistical machine translation.
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. 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. He is the lead developer on mahotas, the popular computer vision package for Python, and has contributed several machine learning codes.
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 readers exactly how to find patterns through raw data. The book starts by brushing up on Python ML knowledge and introducing libraries, and then moves on to more serious projects on datasets, modeling, recommendations, improving recommendations through examples, and going through sound and image processing in detail.
Written for Python programmers, Building Machine Learning Systems with Python teaches readers 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 Stack Overflow answers or whether a music file is Jazz or Metal. They will learn regression, which is demonstrated through how to recommend movies to users. Advanced topics such as topic modeling (finding the most important topics in a text), basket analysis, and cloud computing are covered as well as many other interesting aspects.
Building Machine Learning System with Python covers the following essential topics:
Chapter 1: Getting Started with Python Machine Learning
Chapter 2: Learning How to Classify with Real-world Examples
Chapter 3: Clustering – Finding Related Posts
Chapter 4: Topic Modeling
Chapter 5: Classification – Detecting Poor Answers
Chapter 6: Classification II – Sentiment Analysis
Chapter 7: Regression – Recommendations
Chapter 8: Regression – Recommendations Improved
Chapter 9: Classification III – Music Genre Classification
Chapter 10: Computer Vision – Pattern Recognition
Chapter 11: Dimensionality Reduction
Chapter 12: Big(ger) Data
Appendix: Where to Learn More about Machine Learning
This book is primarily aimed at Python developers who want to learn and build machine learning in their projects, or who want to provide machine learning support to their existing projects, and see them implemented effectively. More information about the book can be found on the Packt book web-page.
|Building Machine Learning System with Python|
|Master Machine Learning using a broad set of Python libraries and start building your own Python-based ML systems
For more information, please visit book page