Machine Learning for the Web
暫譯: 網路上的機器學習

Key Features

• Targets two big and prominent markets where sophisticated web apps are of need and importance.
• Practical examples of building machine learning 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 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.

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++, Java, JavaScript, C#, SQL, HTML, and Hadoop.

Table of Contents

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