Recommender Systems with Machine Learning [Video]
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-
Free ChapterIntroduction
-
Motivation for Recommender System
-
Basic of Recommender Systems
- Section Overview
- Taxonomy of Recommender Systems
- ICM
- User Rating Matrix
- Quality of Recommender Systems
- Online Evaluation Techniques
- Offline Evaluation Techniques
- Data Partitioning
- Important Parameters
- Error Metric Computation
- Content-Based Filtering
- Collaborative Filtering and User-Based Collaborative Filtering
- Item Model and Memory-Based Collaborative Filtering
- Quiz
- Quiz Solution
-
Machine Learning for Recommender System
- Overview
- Benefits of Machine Learning
- Guidelines for ML
- Design Approaches for ML
- Content-Based Filtering
- Data Preparation for Content-Based Filtering
- Data Manipulation for Content-Based Filtering
- Exploring Genres in Content-Based Filtering
- tf-idf (Term Frequency-Inverse Document Frequency) Matrix
- Recommendation Engine
- Making Recommendations
- Item-Based Collaborative Filtering
- Item-Based Filtering Data Preparation
- Age Distribution for Users
- Collaborative Filtering Using KNN
- Geographic Filtering
- KNN Implementation
- Making Recommendations with Collaborative Filtering
- User-Based Collaborative Filtering
- Quiz
- Quiz Solution
-
Project 1: Song Recommendation System Using Content-Based Filtering
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Project 2: Movie Recommendation System Using Collaborative Filtering
About this
video
Have you ever thought how YouTube adjusts your feed as per your favorite content?
Ever wondered! Why is your Netflix recommending your favorite TV shows?
Have you ever wanted to build a customized recommender system for yourself?
Then this is the course you are looking for.
We will begin with the theoretical concepts and fundamental knowledge of recommender systems. You will gain an understanding of the essential taxonomies that form the foundation of these systems. You will be learning how to use the power of Python to evaluate your recommender systems datasets based on user ratings, user choices, music genres, categories of movies, and their year of release. A practical approach will be adopted to build content-based filtering and collaborative filtering techniques for recommender systems.
Moving ahead, you will learn all the basic and necessary concepts for the applied recommender systems models along with the machine learning models. Moreover, various projects have been included in this course to develop a very useful experience for you.
By the end of this course, you will be able to relate the concepts and theories for recommender systems in various domains, implement machine learning models for building real-world recommendation systems, and evaluate the machine learning models.
All the resource files are added to the GitHub repository at: https://github.com/PacktPublishing/Recommender-Systems-with-Machine-Learning
- Publication date:
- March 2023
- Publisher
- Packt
- Duration
- 6 hours 17 minutes
- ISBN
- 9781837631667