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.
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.
To fully benefit from the coverage included in this course, you will need:
Python working knowledge
A basic understanding of HTML and CSS syntax
Ability to run a simple Python script in command line (Terminal)
Understanding of Object-Oriented Programming
This course has the following software requirements:
Editor - Atom / Sublime Text / PyCharm
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:
OS: Windows 10 Pro x64 Version 1803(OS Build 17134.765 ) with a virtualization of Ubuntu 18.04.2 LTS 64 Bits
Processor: Intel Core i7-6700HQ CPU @ 2.60GHz
Memory: 16GB 2133MHz SODIMM
Storage: 512MB SSD