Machine Learning for the Web

Explore the web and make smarter predictions using Python
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Machine Learning for the Web

Andrea Isoni

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Explore the web and make smarter predictions using Python
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Book Details

ISBN 139781785886607
Paperback298 pages

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 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

Chapter 1: Introduction to Practical Machine Learning Using Python
General machine-learning concepts
Preparing, manipulating and visualizing data – NumPy, pandas and matplotlib tutorials
Scientific libraries used in the book
When to use machine learning
Summary
Chapter 2: Unsupervised Machine Learning
Clustering algorithms
Dimensionality reduction
Singular value decomposition
Summary
Chapter 3: Supervised Machine Learning
Model error estimation
Generalized linear models
Naive Bayes
Decision trees
Support vector machine
A comparison of methods
Hidden Markov model
Summary
Chapter 4: Web Mining Techniques
Web structure mining
Web content mining
Natural language processing
Postprocessing information
Summary
Chapter 5: Recommendation Systems
Utility matrix
Similarities measures
Collaborative Filtering methods
CBF methods
Association rules for learning recommendation system
Log-likelihood ratios recommendation system method
Hybrid recommendation systems
Evaluation of the recommendation systems
Summary
Chapter 6: Getting Started with Django
HTTP – the basics of the GET and POST methods
Writing an app – most important features
Admin
Summary
Chapter 7: Movie Recommendation System Web Application
Application setup
Models
Commands
User sign up login/logout implementation
Information retrieval system (movies query)
Rating system
Recommendation systems
Admin interface and API
Summary
Chapter 8: Sentiment Analyser Application for Movie Reviews
Application usage overview
Search engine choice and the application code
Scrapy setup and the application code
Django models
Integrating Django with Scrapy
PageRank: Django view and the algorithm code
Admin and API
Summary

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

Authors

Table of Contents

Chapter 1: Introduction to Practical Machine Learning Using Python
General machine-learning concepts
Preparing, manipulating and visualizing data – NumPy, pandas and matplotlib tutorials
Scientific libraries used in the book
When to use machine learning
Summary
Chapter 2: Unsupervised Machine Learning
Clustering algorithms
Dimensionality reduction
Singular value decomposition
Summary
Chapter 3: Supervised Machine Learning
Model error estimation
Generalized linear models
Naive Bayes
Decision trees
Support vector machine
A comparison of methods
Hidden Markov model
Summary
Chapter 4: Web Mining Techniques
Web structure mining
Web content mining
Natural language processing
Postprocessing information
Summary
Chapter 5: Recommendation Systems
Utility matrix
Similarities measures
Collaborative Filtering methods
CBF methods
Association rules for learning recommendation system
Log-likelihood ratios recommendation system method
Hybrid recommendation systems
Evaluation of the recommendation systems
Summary
Chapter 6: Getting Started with Django
HTTP – the basics of the GET and POST methods
Writing an app – most important features
Admin
Summary
Chapter 7: Movie Recommendation System Web Application
Application setup
Models
Commands
User sign up login/logout implementation
Information retrieval system (movies query)
Rating system
Recommendation systems
Admin interface and API
Summary
Chapter 8: Sentiment Analyser Application for Movie Reviews
Application usage overview
Search engine choice and the application code
Scrapy setup and the application code
Django models
Integrating Django with Scrapy
PageRank: Django view and the algorithm code
Admin and API
Summary

Book Details

ISBN 139781785886607
Paperback298 pages
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