Dimensionality reduction can be roughly grouped into feature selection and feature extraction methods. We have already employed some kind of feature selection in almost every chapter when we invented, analyzed, and then probably dropped some features. In this chapter, we will present some ways that use statistical methods, namely correlation and mutual information, to be able to do feature selection in vast feature spaces. Feature extraction tries to transform the original feature space into a lower-dimensional feature space. This is useful especially when we cannot get rid of features using selection methods, but we still have too many features for our learner. We will demonstrate this using principal component analysis (PCA), linear discriminant analysis (LDA), and multidimensional scaling (MDS).
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