Machine Learning – Recommendation Systems in Python


Understand How Online Recommendations Work by Building a Movie App

In this ’Recommendation Systems in Python’ online course,  you’ll learn about key concepts such as content-based filtering, collaborative filtering, neighborhood models, matrix factorization, and more! By the time you’ve finished the training, you’ll be able to build a movie recommendation system in Python by mastering both theory and practice.  Supplemental Material included!

Length: 4 hrs 30 min

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Recommendation Engines perform a variety of tasks, but the most important one is to find products that are most relevant to the user. Follow along with this intensive Recommendation Systems in Python training course to get a firm grasp on this essential Machine Learning component.

Course Info


  • No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided.


  • Learn about Movielens – a famous dataset with movie ratings
  • Use Pandas to read and play around with the data
  • Learn how to use Scipy and Numpy
  • Introduction to Latent Factor Methods
  • Introduction to Memory-based Approaches
  • Design & implement a Recommendation System in Python

Target Audience:

  • Analytics professionals, modelers, big data professionals who haven’t had exposure to machine learning
  • Engineers who want to understand or learn machine learning and apply it to problems they are solving
  • Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
  • Tech executives and investors who are interested in big data, machine learning or natural language processing
  • MBA graduates or business professionals who are looking to move to a heavily quantitative role

Sample clip


Chapter 01: Would You Recommend to a Friend?

Lesson 01: Introduction: You, This Course & Us!

Lesson 02: What do Amazon and Netflix have in common?

Lesson 03: Recommendation Engines: a look inside

Lesson 04: What are you made of? Content-Based Filtering

Lesson 05: With a little help from friends: Collaborative Filtering

Lesson 06: A Model for Collaborative Filtering

Lesson 07: Top Picks for You! Recommendations with Neighborhood Models

Lesson 08: Discover the Underlying Truth: Latent Factor Collaborative Filtering

Lesson 09: Latent Factor Collaborative Filtering continued

Lesson 10: Gray Sheep & Shillings: Challenges with Collaborative Filtering

Lesson 11: The Apriori Algorithm for Association Rules

Chapter 02: Recommendation Systems in Python

Lesson 01: Installing Python : Anaconda & PIP

Lesson 02: Back to Basics: Numpy in Python

Lesson 03: Back to Basics: Numpy & Scipy in Python

Lesson 04: Movielens & Pandas

Lesson 05: Code Along: What’s my favorite movie? – Data Analysis with Pandas

Lesson 06: Code Along: Movie Recommendation with Nearest Neighbor CF

Lesson 07: Code Along: Top Movie Picks (Nearest Neighbor CF)

Lesson 08: Code Along: Movie Recommendations with Matrix Factorization

Lesson 09: Code Along: Association Rules with the Apriori Algorithm

Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi, and Navdeep Singh have honed their tech expertise at Google and Flipkart. Together, they have created dozens of training courses and are excited to be sharing their content with eager students. The team believes it has distilled the instruction of complicated tech concepts into enjoyable, practical, and engaging courses.

Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft

Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too

Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum

Navdeep: Longtime Flipkart employee too, and IIT Guwahati alum