Let us help you find the training program you are looking for.

If you can't find what you are looking for, contact us, we'll help you find it. We have over 800 training programs to choose from.


Course Skill Level:


Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Those experienced in Python and recommendation systems with basic IT & Linux skills

Who should attend & recommended skills

  • Python experienced developers, analysts or others with Recommendation Systems and Python skills who are intending to learn the tools and techniques required in building various kinds of powerful recommendation systems (collaborative, knowledge and content based), and deploying them to the web.
  • Skill-level: Foundation-level Recommendation Systems with Python for Intermediate skilled team members. This is not a basic class.
  • IT skills: Basic to Intermediate (1-5 years)
  • Linux: Basic (1-2 years), including familiarity with command-line options such as ls, cd, cp, and su
  • Python: Basic (1-2 years) is required.
  • Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them

About this course

By the time you’re finished, you will have a great grasp of how recommenders work and be in a strong position to apply the techniques that you will learn to your own problem domains.
No need to wade through complicated machine learning theory to use this course.
Recommendation systems are at the heart of almost every internet business today; from Facebook to Netflix to Amazon. Providing good recommendations, whether it’s 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’ll 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’ll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques.

Skills acquired & topics covered

  • Building industry-standard recommender systems
  • Getting to grips with the different kinds of recommender systems
  • Mastering data-wrangling techniques using the pandas library
  • Building an IMDB Top 250 Clone
  • Building a content-based engine to recommend movies based on movie metadata
  • Employing data-mining techniques used in building recommenders
  • Building industry-standard collaborative filters using powerful algorithms
  • Building Hybrid Recommenders that incorporate content based and collaborative filtering

Course breakdown / modules

  • Technical requirements
  • What is a recommender system?
  • Types of recommender systems

  • Technical requirements
  • Setting up the environment
  • The Pandas library
  • The Pandas DataFrame
  • The Pandas Series

  • Technical requirements
  • The simple recommender
  • The knowledge-based recommender

  • Technical requirements
  • Exporting the clean DataFrame
  • Document vectors
  • The cosine similarity score
  • Plot description-based recommender
  • Metadata-based recommender
  • Suggestions for improvements

  • Problem statement
  • Similarity measures
  • Clustering
  • Dimensionality reduction
  • Supervised learning
  • Evaluation metrics

  • Technical requirements
  • The framework
  • User-based collaborative filtering
  • Item-based collaborative filtering
  • Model-based approaches

  • Technical requirements
  • Introduction
  • Case study – Building a hybrid model