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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.

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Last Updated

March 11, 2021

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Certification

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
    • Technical requirements 00:00:00
    • Exporting the clean DataFrame 00:00:00
    • Document vectors 00:00:00
    • The cosine similarity score 00:00:00
    • Plot description-based recommender 00:00:00
    • Metadata-based recommender 00:00:00
    • Suggestions for improvements 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
    • The framework 00:00:00
    • User-based collaborative filtering 00:00:00
    • Item-based collaborative filtering 00:00:00
    • Model-based approaches 00:00:00
    • Technical requirements 00:00:00
    • Introduction 00:00:00
    • Case study and final project – Building a hybrid model 00:00:00

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