Book description
Explore the web and make smarter predictions using Python
About This Book
- 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.
Who This Book Is For
The book is aimed at upcoming and new data scientists who have little experience with machine learning or users who are interested in and are working on developing smart (predictive) web applications. Knowledge of Django would be beneficial. The reader is expected to have a background in Python programming and good knowledge of statistics.
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
In Detail
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.
Style and approach
Instead of being overwhelmed with multiple concepts at once, this book provides a step-by-step approach that will guide you through one topic at a time.
An intuitive step-by step guide that will focus on one key topic at a time. Building upon the acquired knowledge in each chapter, we will connect the fundamental theory and practical tips by illustrative visualizations and hands-on code examples.
Table of contents
-
Machine Learning for the Web
- Table of Contents
- Machine Learning for the Web
- Credits
- Foreword
- About the Author
- About the Reviewers
- www.PacktPub.com
- Preface
- 1. Introduction to Practical Machine Learning Using Python
- 2. Unsupervised Machine Learning
- 3. Supervised Machine Learning
- 4. Web Mining Techniques
-
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
- 6. Getting Started with Django
- 7. Movie Recommendation System Web Application
- 8. Sentiment Analyser Application for Movie Reviews
- Index
Product information
- Title: Machine Learning for the Web
- Author(s):
- Release date: July 2016
- Publisher(s): Packt Publishing
- ISBN: 9781785886607
You might also like
book
Hands-On Machine Learning with TensorFlow.js
Get hands-on with the browser-based JavaScript library for training and deploying machine learning models effectively Key …
book
Python Machine Learning - Second Edition
Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open …
book
Machine Learning Algorithms - Second Edition
An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms …
book
Python Machine Learning Cookbook - Second Edition
Discover powerful ways to effectively solve real-world machine learning problems using key libraries including scikit-learn, TensorFlow, …