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  • Book Overview & Buying Machine Learning for the Web
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Machine Learning for the Web

Machine Learning for the Web

By : Steve Essinger, Isoni
4.5 (27)
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Machine Learning for the Web

Machine Learning for the Web

4.5 (27)
By: Steve Essinger, Isoni

Overview of this book

Python is a general purpose and also a comparatively easy to learn programming language. Hence it is the language of choice for data scientists to prototype, visualize, and run data analyses on small and medium-sized data sets. This is a unique book that helps bridge the gap between machine learning and web development. It focuses on the difficulties of implementing predictive analytics in web applications. We focus on the Python language, frameworks, tools, and libraries, showing you how to build a machine learning system. You will explore the core machine learning concepts and then develop and deploy the data into a web application using the Django framework. You will also learn to carry out web, document, and server mining tasks, and build recommendation engines. Later, you will explore Python’s impressive Django framework and will find out how to build a modern simple web app with machine learning features.
Table of Contents (10 chapters)
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9
Index

Dimensionality reduction

Dimensionality reduction, which is also called feature extraction, refers to the operation to transform a data space given by a large number of dimensions to a subspace of fewer dimensions. The resulting subspace should contain only the most relevant information of the initial data, and the techniques to perform this operation are categorized as linear or non-linear. Dimensionality reduction is a broad class of techniques that is useful for extracting the most relevant information from a large dataset, decreasing its complexity but keeping the relevant information.

The most famous algorithm, Principal Component Analysis (PCA), is a linear mapping of the original data into a subspace of uncorrelated dimensions, and it will be discussed hereafter. The code shown in this paragraph is available in IPython notebook and script versions at the author's GitHub book folder at https://github.com/ai2010/machine_learning_for_the_web/tree/master/chapter_2/.

Principal Component...

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Machine Learning for the Web
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