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Packt Building Recommendation Systems With Python

Added by Minhchick in category: Video Tutorial (6-07-2019, 16:42)
Packt Building Recommendation Systems With Python

Packt Building Recommendation Systems With Python-JGTiSO
English | Size: 357.47 MB
Category: Tutorial

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


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 -
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.

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

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