Machine Learning for the Web

Machine Learning for the Web Front Cover
19 Reviews
2016-07-29
298 pages

Book Description

Key Features

  • Targets two big and prominent markets where sophisticated are of need and importance.
  • Practical examples of building web application, which are easy to follow and replicate.
  • A comprehensive tutorial on Python libraries and frameworks to get you up and started.

Book Description

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 framework. You will also learn to carry out web, , 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.

What you will learn

  • Get familiar with the fundamental concepts and some of the jargons used in the machine learning community
  • Use tools and techniques to mine data from websites
  • Grasp the core concepts of Django framework
  • Get to know the most useful clustering and classification techniques and implement them in Python
  • Acquire all the necessary knowledge to build a web application with Django
  • Successfully build and deploy a movie recommendation system application using the Django framework in Python

About the Author

Andrea Isoni is a data scientist, PhD, and physicist professional with extensive experience in software developer positions. He has an extensive knowledge of machine learning algorithms and techniques. He also has experience with multiple languages, such as Python, C/C++, , JavaScript, C#, , HTML, and Hadoop.

Table of Contents

Chapter 1. Introduction to Practical Machine Learning Using Python
Chapter 2. Unsupervised Machine Learning
Chapter 3. Supervised Machine Learning
Chapter 4. Web Mining Techniques
Chapter 5. Recommendation Systems
Chapter 6. Getting Started with Django
Chapter 7. Movie Recommendation System Web Application
Chapter 8. Sentiment Analyser Application for Movie Reviews

Book Details

  • Title: Machine Learning for the Web
  • Author:
  • Length: 298 pages
  • Edition: 1
  • Language: English
  • Publisher:
  • Publication Date: 2016-07-29
  • ISBN-10: B01BVHNOBM
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