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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

Summary

In this chapter we introduced the basic machine-learning concepts and terminology that will be used in the rest of the book. Tutorials of the most relevant libraries (NumPy, pandas, and matplotlib) used by machine-learning professionals to prepare, t manipulate, and visualize data have been also presented. A general introduction of all the other Python libraries that will be used in the following chapters has been also provided.

You should have a general knowledge of what the machine-learning field can practically do, and you should now be familiar with the methods employed to transform the data into a usable format, so that a machine-learning algorithm can be applied. In the next chapter we will explain the main unsupervised learning algorithms and how to implement them using the sklearn library.

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Machine Learning for the Web
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