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!
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.
- 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
Chapter I: Would You Recommend to a Friend?
- Lesson I: Introduction: You, This Course & Us!
- Lesson II: What do Amazon and Netflix have in common?
- Lesson III: Recommendation Engines: a look inside
- Lesson IV: What are you made of? Content-Based Filtering
- Lesson V: With a little help from friends: Collaborative Filtering
- Lesson VI: A Model for Collaborative Filtering
- Lesson VII: Top Picks for You! Recommendations with Neighborhood Models
- Lesson VIII: Discover the Underlying Truth: Latent Factor Collaborative Filtering
- Lesson IX: Latent Factor Collaborative Filtering continued
- Lesson X: Gray Sheep & Shillings: Challenges with Collaborative Filtering
- Lesson XI: The Apriori Algorithm for Association Rules
Chapter II: Recommendation Systems in Python
- Lesson I: Installing Python : Anaconda & PIP
- Lesson II: Back to Basics: Numpy in Python
- Lesson III: Back to Basics: Numpy & Scipy in Python
- Lesson IV: Movielens & Pandas
- Lesson V: Code Along: What’s my favorite movie? – Data Analysis with Pandas
- Lesson VI: Code Along: Movie Recommendation with Nearest Neighbor CF
- Lesson VII: Code Along: Top Movie Picks (Nearest Neighbor CF)
- Lesson VIII: Code Along: Movie Recommendations with Matrix Factorization
- Lesson IX: Code Along: Association Rules with the Apriori Algorithm
4 hrs 30 min
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
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