Congratulations for sticking with us until the end! Together we have learned how Naive Bayes work and why they are not that naive at all. For training sets where we don't have enough data to learn all the niches in the class probability space, Naive Bayes do a great job of generalizing. We learned how to apply them to tweets and that cleaning the rough tweets' text helps a lot. Finally, we realized that a bit of "cheating" (only after we have done our fair share of work) is OK, especially, when it gives another improvement of the classifier's performance, as we have experienced with the use of SentiWordNet.
Building Machine Learning Systems with Python
Building Machine Learning Systems with Python
Overview of this book
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.
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Table of Contents (20 chapters)
Building Machine Learning Systems with Python
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Getting Started with Python Machine Learning
Learning How to Classify with Real-world Examples
Clustering – Finding Related Posts
Topic Modeling
Classification – Detecting Poor Answers
Classification II – Sentiment Analysis
Regression – Recommendations
Regression – Recommendations Improved
Classification III – Music Genre Classification
Computer Vision – Pattern Recognition
Dimensionality Reduction
Big(ger) Data
Where to Learn More about Machine Learning
Index
Customer Reviews