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.
- 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
- 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
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