Course: Recommender Systems Complete Course Beginner to Advanced
Modules:
Module and Instructor Introduction, AI Sciences, Course Outline, Machine Learning Recommender Systems, Deep Learning Recommender Systems
Motivation for Recommender System: Recommender Systems Overview, Motivation for Recommender System: Introduction to Recommender Systems, Motivation for Recommender System: Recommender Systems Process and Goals, Motivation for Recommender System: Generations of Recommender Systems, Motivation for Recommender System: Nexus of AI and Recommender Systems, Motivation for Recommender System: Applications and Real-World Challenges, Motivation for Recommender System: Quiz, Motivation for Recommender System: Quiz Solution, Basics of Recommender System: Overview, Basics of Recommender System: Taxonomy of Recommender Systems, Basics of Recommender System: ICM, Basics of Recommender System: User Rating Matrix, Basics of Recommender System: Quality of Recommender System, Basics of Recommender System: Online Evaluation Techniques, Basics of Recommender System: Offline Evaluation Techniques, Basics of Recommender System: Data Partitioning, Basics of Recommender System: Important Parameters, Basics of Recommender System: Error Metric Computation, Basics of Recommender System: Content-Based Filtering, Basics of Recommender System: Collaborative Filtering and User-Based Collaborative Filtering, Basics of Recommender System: Item Model and Memory-Based Collaborative Filtering, Basics of Recommender System: Quiz, Basics of Recommender System: Quiz Solution, Machine Learning for Recommender Systems: Overview, Machine Learning for Recommender Systems: Benefits of Machine Learning, Machine Learning for Recommender Systems: Guidelines for ML, Machine Learning for Recommender Systems: Design Approaches for ML, Machine Learning for Recommender Systems: Content-Based Filtering, Machine Learning for Recommender Systems: Data Preparation for Content-Based Filtering, Machine Learning for Recommender Systems: Data Manipulation for Content-Based Filtering, Machine Learning for Recommender Systems: Exploring Genres in Content-Based Filtering, Machine Learning for Recommender Systems: tf-idf Matrix, Machine Learning for Recommender Systems: Recommendation Engine, Machine Learning for Recommender Systems: Making Recommendations, Machine Learning for Recommender Systems: Item-Based Collaborative Filtering, Machine Learning for Recommender Systems: Item-Based Filtering Data Preparation, Machine Learning for Recommender Systems: Age Distribution for Users, Machine Learning for Recommender Systems: Collaborative Filtering using KNN, Machine Learning for Recommender Systems: Geographic Filtering, Machine Learning for Recommender Systems: KNN Implementation, Machine Learning for Recommender Systems: Making Recommendations with Collaborative Filtering, Machine Learning for Recommender Systems: User-Based Collaborative Filtering, Machine Learning for Recommender Systems: Quiz, Machine Learning for Recommender Systems: Quiz Solution, Project 1: Song Recommendation System Using Content-Based Filtering: Project Introduction, Project 1: Song Recommendation System Using Content-Based Filtering: Dataset Usage, Project 1: Song Recommendation System Using Content-Based Filtering: Missing Values, Project 1: Song Recommendation System Using Content-Based Filtering: Exploring Genres, Project 1: Song Recommendation System Using Content-Based Filtering: Occurrence Count, Project 1: Song Recommendation System Using Content-Based Filtering: tf-idf Implementation, Project 1: Song Recommendation System Using Content-Based Filtering: Similarity Index, Project 1: Song Recommendation System Using Content-Based Filtering: Fuzzywuzzy Implementation, Project 1: Song Recommendation System Using Content-Based Filtering: Find Closest Title, Project 1: Song Recommendation System Using Content-Based Filtering: Making Recommendations, Project 2: Movie Recommendation System Using Collaborative Filtering: Project Introduction, Project 2: Movie Recommendation System Using Collaborative Filtering: Dataset Discussion, Project 2: Movie Recommendation System Using Collaborative Filtering: Rating Plot, Project 2: Movie Recommendation System Using Collaborative Filtering: Count, Project 2: Movie Recommendation System Using Collaborative Filtering: Logarithm of Count, Project 2: Movie Recommendation System Using Collaborative Filtering: Active Users and Popular Movies, Project 2: Movie Recommendation System Using Collaborative Filtering: Create Collaborative Filter, Project 2: Movie Recommendation System Using Collaborative Filtering: KNN Implementation, Project 2: Movie Recommendation System Using Collaborative Filtering: Making Recommendations
Deep Learning Foundation for Recommender Systems: Module Introduction, Deep Learning Foundation for Recommender Systems: Overview, Deep Learning Foundation for Recommender Systems: Deep Learning in Recommendation systems, Deep Learning Foundation for Recommender Systems: Inference After Training, Deep Learning Foundation for Recommender Systems: Inference Mechanism, Deep Learning Foundation for Recommender Systems: Embeddings and User Context, Deep Learning Foundation for Recommender Systems: Neural Collaborative Filtering, Deep Learning Foundation for Recommender Systems: VAE Collaborative Filtering, Deep Learning Foundation for Recommender Systems: Strengths and Weaknesses of DL Models, Deep Learning Foundation for Recommender Systems: Deep Learning Quiz, Deep Learning Foundation for Recommender Systems: Deep Learning Quiz Solution, Project Amazon Product Recommendation System: Module Overview, Project Amazon Product Recommendation System: TensorFlow Recommenders, Project Amazon Product Recommendation System: Two-Tower Model, Project Amazon Product Recommendation System: Project Overview, Project Amazon Product Recommendation System: Download Libraries, Project Amazon Product Recommendation System: Data Visualization with WordCloud, Project Amazon Product Recommendation System: Make Tensors from DataFrame, Project Amazon Product Recommendation System: Rating Our Data, Project Amazon Product Recommendation System: Random Train-Test Split, Project Amazon Product Recommendation System: Making the Model and Query Tower, Project Amazon Product Recommendation System: Candidate Tower and Retrieval System, Project Amazon Product Recommendation System: Compute Loss, Project Amazon Product Recommendation System: Train and Validation, Project Amazon Product Recommendation System: Accuracy Versus Recommendations, Project Amazon Product Recommendation System: Making Recommendations