Understand How Online Recommendations Work by Building a Movie App with Python!
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.
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.
What the course will teach you:
- 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
- Much More..
AI Machine Learning – Recommendation Systems in Python
Course Length: 4.5 Hours
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
Minimum specifications for the computer are:
Microsoft Windows XP, or later
Modern and up to date Browser (Internet Explorer 8 or later, Firefox, Chrome, Safari)
OSX/iOS 6 or later
Modern and up to date Browser (Firefox, Chrome, Safari)
Internet bandwidth of 1Mb or faster
Flash player or a browser with HTML5 video capabilities (We recommend Google Chrome)