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Building Recommendation Systems with Python (TTAI2360)

Recommendation systems are at the heart of almost every internet business today; from Facebook to Net ix to Amazon. Providing good recommendations, whether its friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.This course shows you how to do...

  • 4.6 out of 5 rating
  • Last updated : 21/01/2026
  • English

Available as Instructor Led Training, Live Online & In Person at your Offices or Ours.

Building Recommendation Systems with Python (TTAI2360)
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Duration:

24.00 hours

19.5 CPD hours

Overview:

Recommendation systems are at the heart of almost every internet business today; from Facebook to Net ix to Amazon. Providing good recommendations, whether its friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.This course shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory you will get started with building and learning about recommenders as quickly as possible. In this course, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You will also use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques. Students will learn to build industry-standard recommender systems, leveraging basic Python syntax skills. This is an applied course, so machine learning theory is only used to highlight how to build recommenders in this course.

Description:

Recommendation systems are at the heart of almost every internet business today; from Facebook to Net ix to Amazon. Providing good recommendations, whether its friends, movies, or groceries, goes a long way in defining user experience and enticing your customers to use your platform.This course shows you how to do just that. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory you will get started with building and learning about recommenders as quickly as possible. In this course, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You will also use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques. Students will learn to build industry-standard recommender systems, leveraging basic Python syntax skills. This is an applied course, so machine learning theory is only used to highlight how to build recommenders in this course.
DURATION 1 Day

No upcoming local classes scheduled.

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Training Insurance Included!

When you organise training, we understand that there is a risk that some people may fall ill, become unavailable.

To mitigate the risk we include training insurance for each delegate enrolled on our public schedule, they are welcome to sit on the same Public class within 6 months at no charge, if the case arises.