Building Recommendation Systems with Python [Video]
This is the code repository for Building Recommendation Systems with Python [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
About the Video Course
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.
What You Will 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 projects
- 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
Instructions and Navigation
Assumed Knowledge
To fully benefit from the coverage included in this course, you will need:
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Python working knowledge
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A basic understanding of HTML and CSS syntax
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Ability to run a simple Python script in command line (Terminal)
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Understanding of Object-Oriented Programming
Technical Requirements
This course has the following software requirements:
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Editor - Atom / Sublime Text / PyCharm
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PIP and NumPy: Installed with PIP, Ubuntu*, Python 3.6.2, NumPy 1.13.1, scikit-learn 0.18.2
This course has the following system requirements:
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OS: Windows 10 Pro x64 Version 1803(OS Build 17134.765 ) with a virtualization of Ubuntu 18.04.2 LTS 64 Bits
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Processor: Intel Core i7-6700HQ CPU @ 2.60GHz
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Memory: 16GB 2133MHz SODIMM
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Storage: 512MB SSD