Packt - Building Recommendation Systems with Python

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Packt - Building Recommendation Systems with Python
English | Size: 593.52 MB
Category: CBTs

Learn
Build your own recommendation engine with Python to analyze data
Use effective text-mining tools to get the best raw data
Master collaborative filtering techniques based on user profiles and the item they want
Content-based filtering techniques that use user data such as comments and ratings
Hybrid filtering technique which combines both collaborative and content-based filtering
Utilize Pandas and sci-kit-learn easy-to-use data structures for data analysis

About
Recommendation Engines have become an integral part of any application. For accurate recommendations, you require user information. The more data you feed to your engine, the more output it can generate - for example, a movie recommendation based on its rating, a YouTube video recommendation to a viewer, or recommending a product to a shopper online.

Packt - Building Recommendation Systems with Python
English | Size: 593.52 MB
Category: CBTs

Learn
Build your own recommendation engine with Python to analyze data
Use effective text-mining tools to get the best raw data
Master collaborative filtering techniques based on user profiles and the item they want
Content-based filtering techniques that use user data such as comments and ratings
Hybrid filtering technique which combines both collaborative and content-based filtering
Utilize Pandas and sci-kit-learn easy-to-use data structures for data analysis

About
Recommendation Engines have become an integral part of any application. For accurate recommendations, you require user information. The more data you feed to your engine, the more output it can generate - for example, a movie recommendation based on its rating, a YouTube video recommendation to a viewer, or recommending a product to a shopper online.

In this practical course, you will be building three powerful real-world recommendation engines using three different filtering techniques. You'll start by creating usable data from your data source and implementing the best data filtering techniques for recommendations. Then you will use Machine Learning techniques to create your own algorithm, which will predict and recommend accurate data.

By the end of the course, you'll be able to build effective online recommendation engines with Machine Learning and Python - on your own.

The code bundle for this video course is available at - https://github.com/PacktPublishing/Building-Recommendation-Systems-with-Python
Style and Approach

This course is a step-by-step guide to building your own recommendation engine with Python. It will help you gain all the training and skills you need to make suggestions as to data that a website user might be interested in, by using various data filtering techniques.
Features

Understand how to work with real data using a recommendation in Python
Graphical representation of categories or classes to visualize your data
Comparison of different recommender systems and learning to help you choose the right one

Course Length 1 hour 35 minutes
ISBN 9781788991704
Date Of Publication 30 May 2019

Table Of Contents:

1. Get Started with Text Mining and Cleaning Data
Exploring Recommendation Engines
Working with Variables You Are Taking into Consideration
Setting Up Your Working Environment
Understanding Text Data Source and Variables
Imputation Methods for Missing Data

2. Collaborative Filtering-Based Recommender System
Exploring the Required Functions - Logic
Implementation of CF Recommender System
Applying the CF Algorithm to the IMDBs Dataset
Evaluating the Collaborative Filtering Recommender

3. Content and Popularity Based Recommender Systems
Implementing the Content-Based Recommender System
Understanding Popularity-Based Recommender System
Implementing the Popularity-Based Recommender System
Evaluating Content-Based and Popularity-Based Recommender Systems

4. Hybrid Recommender System
Working with the Required Functions - Logic
Algorithm Implementation for Hybrid Recommender System
Implementation of the Hybrid Recommender System
Evaluating the Hybrid Recommender System

5. Flask Web Application Using PyCharm
Setting Up the Integrated Development Environment
Creating a Web Application Using Flask
Implementation of a Web Application Using Flask

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