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Data Sciences

Recommendation Systems with Python

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Working in a hands-on learning environment, led by our Recommendation Systems expert instructor, students will learn about and explore:
Build industry-standard recommender systems.Only familiarity with Python is required.No need to wade through complicated machine learning theory to use this course.

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Course Access

Unlimited Duration

Last Updated

July 29, 2021

Students Enrolled

20

Total Reviews

Posted by

Ernesto Lee

Certification

Recommendation systems are at the heart of almost every internet business today; from Facebook to Netflix to Amazon. Providing good recommendations, whether it's 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. You will learn about the different kinds of recommenders used in the industry and see how to build them from scratch using Python. No need to wade through tons of machine learning theory—you'll get started with building and learning about recommenders as quickly as possible. In this course, you will build an IMDB Top 250 clone, a content-based engine that works on movie metadata. You'll use collaborative filters to make use of customer behavior data, and a Hybrid Recommender that incorporates content based and collaborative filtering techniques

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
    • 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 – Building a hybrid model 00:00:00

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