Course Details of on Movie Recommendation Systems using machine learning.
Course Description:
This course will cover the concepts, algorithms, and techniques used in designing and building movie recommendation systems. Students will learn the basics of recommendation systems, including content-based filtering, collaborative filtering, and matrix factorization. They will also learn how to implement and evaluate these techniques using real-world datasets.
Course Objectives:
Understand the basic concepts of recommendation systems
Learn how to build content-based and collaborative filtering systems
Learn how to use matrix factorization to improve recommendation accuracy
Understand how to evaluate recommendation systems using different metrics
Implement recommendation systems using real-world datasets
Learn about current research topics in recommendation systems
Course Outline:
Introduction to Recommendation Systems
Overview of recommendation systems
Types of recommendation systems
Applications of recommendation systems
Content-based Filtering
Representing items and users
Content-based recommendation algorithms
Evaluating content-based filtering
Collaborative Filtering
User-item interaction data
Memory-based and model-based collaborative filtering
Evaluating collaborative filtering
Matrix Factorization
Matrix factorization models
Singular value decomposition (SVD)
Non-negative matrix factorization (NMF)
Hybrid Recommendation Systems
Combining multiple recommendation techniques
Case studies of hybrid recommendation systems
Evaluation Metrics
Accuracy metrics (e.g., RMSE, MAE)
Ranking metrics (e.g., precision, recall)
User engagement metrics (e.g., click-through rate)
Implementing Recommendation Systems
Data preprocessing and feature engineering
Building a recommendation engine
Deploying a recommendation system
Advanced Topics
Deep learning-based recommendation systems
Context-aware recommendation systems
Explainable recommendation systems
Prerequisites:
Basic knowledge of linear algebra and probability theory
Programming experience in Python
If you are interested in designing and building intelligent systems that can recommend movies to users, then taking a course on Movie Recommendation Systems is an excellent opportunity to gain the skills and knowledge needed for this field. By enrolling in this course, you will learn about the different recommendation algorithms, their advantages and limitations, and the techniques used to evaluate their performance. You will also get hands-on experience working with real-world datasets and implementing recommendation systems using Python.
Don't miss this chance to become an expert in movie recommendation systems and enhance your career prospects in the fields of artificial intelligence, data science, and machine learning. Enroll now and take the first step towards building personalized and engaging recommendation systems for movie lovers!
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