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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

Web structure mining

This field of web mining focuses on the discovery of the relationships among web pages and how to use this link structure to find the relevance of web pages. For the first task, usually a spider is employed, and the links and the collected web pages are stored in a indexer. For the the last task, the web page ranking evaluates the importance of the web pages.

Web crawlers (or spiders)

A spider starts from a set of URLs (seed pages) and then extracts the URL inside them to fetch more pages. New links are then extracted from the new pages and the process continues until some criteria are matched. The unvisited URLs are stored in a list called frontier, and depending on how the list is used, we can have different crawler algorithms, such as breadth-first and preferential spiders. In the breadth-first algorithm, the next URL to crawl comes from the head of the frontier while the new URLs are appended to the frontier tail. Preferential spider instead employs a certain importance...

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Machine Learning for the Web
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