Course: Building Recommender Systems with Machine Learning and AI
Modules:
Installing Anaconda, Course Materials, and Creating Movie Recommendations, Course Roadmap, What Is a Recommender System?, Types of Recommenders, Understanding the Implicit and Explicit Ratings, Top-N Recommender Architecture, Review the Basics of a Recommender System
The Basics of Python (Activity), Data Structures in Python, Functions in Python, Booleans, Loops, and a Hands-on Challenge (Exercise)
Train/Test and Cross-Validation, Accuracy Metrics Using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), Top-N Hit Rate - Many Ways, Coverage, Diversity, and Novelty, Churn, Responsiveness, and A/B Tests, Reviewing Ways to Measure Your Recommender (Quiz), Walkthrough of RecommenderMetrics.py (Activity), Walkthrough of TestMetrics.py (Activity), Measuring the Performance of Singular Value Decomposition (SVD) Recommendations (Activity)
Recommender Engine Architecture, Recommender Engine Walkthrough - Part 1 (Activity), Recommender Engine Walkthrough - Part 2 (Activity), Reviewing the Results of Our Algorithm Evaluation (Activity)
Content-Based Recommendations and the Cosine Similarity Metric, K-Nearest-Neighbors (KNN) and Content Recommendations, Producing and Evaluating Content-Based Movie Recommendations (Activity), A Note on Using Implicit Ratings, Bleeding Edge Alert! Mise-En-Scene Recommendations (Activity), Diving Deep into Content-Based Recommendations (Exercise)
Measuring Similarity and Sparsity, Similarity Metrics, User-Based Collaborative Filtering, User-Based Collaborative Filtering - Hands-On (Activity), Item-Based Collaborative Filtering, Item-Based Collaborative Filtering - Hands-On (Activity), Tuning Collaborative Filtering Algorithms (Exercise), Evaluating Collaborative Filtering Systems Offline (Activity), Measuring the Hit Rate of Item-based Collaborative Filtering (Exercise), K-Nearest-Neighbor (KNN) Recommenders, Running User-Based and Item-Based K-Nearest-Neighbor (KNN) on MovieLens Dataset (Activity), Experimenting with Different K-Nearest-Neighbor (KNN) Parameters (Exercise), Bleeding Edge Alert! Translation-Based Recommendations
Principal Component Analysis (PCA), Singular Value Decomposition (SVD), Running Singular Value Decomposition (SVD) and Singular Value Decomposition (SVD ++) on MovieLens (Activity), Improving on Singular Value Decomposition (SVD), Tuning the Hyperparameters on Singular Value Decomposition (SVD) (Exercise), Bleeding-Edge Alert! Sparse Linear Methods (SLIM)
Deep Learning Introduction, Deep Learning Prerequisites, History of Artificial Neural Networks (ANN), (Activity) Playing with TensorFlow, Training Neural Networks, Tuning Neural Networks (Avoiding Overfitting), Activation Functions: More Depth, Introduction to TensorFlow, Handwriting Recognition with TensorFlow - Part 1 (Activity), Handwriting Recognition with TensorFlow - Part 2 (Activity), Introduction to Keras, Handwriting Recognition with Keras (Activity), Classifier Patterns with Keras, Predicting Political Parties of Politicians with Keras (Exercise), Introduction to Convolutional Neural Network (CNN), Convolutional Neural Network (CNN) Architecture, Handwriting Recognition with Convolutional Neural Network (CNN) (Activity), Introduction to Recurrent Neural Networks (RNN), Training Recurrent Neural Networks (RNN), Sentiment Analysis of Movie Reviews Using Recurrent Neural Networks (RNN) and Keras (Activity), Tuning Neural Networks, Neural Network Regularization Techniques, Generative Adversarial Networks (GANs), GANs in Action, Generating Images of Clothing with Generative Adversarial Networks (Activity)
Introduction to Deep Learning for Recommenders, Restricted Boltzmann Machine (RBM), Recommendations with Restricted Boltzmann Machine (RBM) - Part 1 (Activity), Recommendations with Restricted Boltzmann Machine (RBM) - Part 2 (Activity), Evaluating the Restricted Boltzmann Machine (RBM) Recommender (Activity), Tuning Restricted Boltzmann Machine (RBM) (Exercise), Exercise Results: Tuning a Restricted Boltzmann Machine (RBM) Recommender, Auto-Encoders for Recommendations: Deep Learning for Recommendation, Recommendations with Deep Neural Networks (Activity), Clickstream Recommendations with Recurrent Neural Networks (RNN), Getting GRU4Rec to Work on Your Desktop (Exercise), Exercise Results: GRU4Rec in Action, Bleeding Edge Alert! Generative Adversarial Networks for Recommendations, TensorFlow Recommenders (TFRS): Introduction and Building a Retrieval Stage, TensorFlow Recommenders (TFRS): Building a Ranking Stage, TensorFlow Recommenders (TFRS): Incorporating Side Features and Deep Retrieval, TensorFlow Recommenders (TFRS): Multi-Task Recommenders, Deep and Cross Networks, ScaNN, and Serving, Bleeding Edge Alert! Deep Factorization Machines, More Emerging Tech to Watch
Introduction and Installation of Apache Spark (Activity), Apache Spark Architecture, Movie Recommendations with Spark, Matrix Factorization, and Alternating Least Squares (ALS) (Activity), Recommendations from 20 Million Ratings with Spark (Activity), Amazon Deep Scalable Sparse Tensor Network Engine (DSSTNE), Amazon Deep Scalable Sparse Tensor Network Engine (DSSTNE) in Action, Scaling Up Amazon Deep Scalable Sparse Tensor Network Engine (DSSTNE), Amazon Web Services (AWS) SageMaker and Factorization Machines, Amazon SageMaker in Action: Factorization Machines on One Million Ratings in the Cloud, Other Systems of Note (Amazon Personalize, RichRelevance, Recombee, and More), Recommender System Architecture
The Cold Start Problem (and Solutions), Implementing Random Exploration (Exercise), Exercise Solution - Random Exploration, Stoplists, Implementing a Stoplist (Exercise), Exercise Solution - Implementing a Stoplist, Filtering Bubbles, Trust, and Outliers, Identifying and Eliminating Outlier Users (Exercise), Exercise Solution: Outlier Removal, Fraud, the Perils of Clickstream, and International Concerns, Temporal Effects and Value-Aware Recommendations
Exploring YouTube's Recommendation System: Challenges and Deep Learning Solutions, How YouTube Uses Deep Learning to Rank and Recommend Videos, How Netflix Uses Hybrid Algorithms to Power Personalized Recommendations, How Netflix Uses Context and Machine Learning to Refine Recommendations
Hybrid Recommenders and Exercise, Exercise Solution: Hybrid Recommenders
More to Explore