ratings
Recommendation systems are at the heart of almost every internet business today; from Facebook to Netflix to Amazon. Providing good recommendations, whether its 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.
Unlimited Duration
March 11, 2021
This skills-focused course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based labs and exercises. In this course you will learn about:
· Understand the different kinds of recommender systems
· Master data-wrangling techniques using the pandas library
· Building an IMDB Top 250 Clone
· Build a content-based engine to recommend movies based on real movie metadata
· Employ data-mining techniques used in building recommenders
· Build industry-standard collaborative filters using powerful algorithms
· Building Hybrid Recommenders that incorporate content based and collaborative filtering
Course Curriculum
-
- Technical requirements 00:00:00
- What is a recommender system? 00:00:00
- Types of recommender systems 00:00:00
-
- Technical requirements 00:00:00
- Setting up the environment 00:00:00
- The Pandas library 00:00:00
- The Pandas DataFrame 00:00:00
- The Pandas Series 00:00:00
- Technical requirements 00:00:00
- The simple recommender 00:00:00
- The knowledge-based recommender 00:00:00
- Problem statement 00:00:00
- Similarity measures 00:00:00
- Clustering 00:00:00
- Dimensionality reduction 00:00:00
- Supervised learning 00:00:00
- Evaluation metrics 00:00:00
- Technical requirements 00:00:00
- Introduction 00:00:00
- Case study and final project – Building a hybrid model 00:00:00
Course Reviews
Students
