AI Recommender Systems Certification Table of Contents

    
Table of Content
 

 

Introduction

  • AI Sciences
  • Course Outline
  • Machine Learning Recommender Systems
  • Deep Learning Recommender Systems

Recommender Systems with Machine Learning

  • Recommender Systems Overview
  • Introduction to Recommender Systems
  • Recommender Systems Process and Goals
  • Generations of Recommender Systems
  • Nexus of AI and Recommender Systems
  • Applications and Real-World Challenges
  • Quiz
  • 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 st 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 for Recommender Systems: An Applied Approach

  • 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


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