Section 1 : Getting Started
Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM
Lecture 2 Note Alternate dataset download location
Lecture 3 [Activity] Install Anaconda, course materials, and create movie recommendations! 00:05:53 Duration
Lecture 4 Course Roadmap 00:03:52 Duration
Lecture 5 What Is a Recommender System 00:02:48 Duration
Lecture 6 Types of Recommenders 00:03:22 Duration
Lecture 7 Understanding You through Implicit and Explicit Ratings 00:04:25 Duration
Lecture 8 About Proctor Testing
Lecture 9 [Quiz] Review the basics of recommender systems 00:04:46 Duration

Section 2 : Introduction to Python [Optional]
Lecture 1 [Activity] The Basics of Python 00:05:04 Duration
Lecture 2 Data Structures in Python 00:05:17 Duration
Lecture 3 Functions in Python 00:02:46 Duration
Lecture 4 [Exercise] Booleans, loops, and a hands-on challenge 00:03:52 Duration

Section 3 : Evaluating Recommender Systems
Lecture 1 TrainTest and Cross Validation 00:03:49 Duration
Lecture 2 Accuracy Metrics (RMSE, MAE) 00:04:06 Duration
Lecture 3 Top-N Hit Rate - Many Ways 00:04:35 Duration
Lecture 4 Coverage, Diversity, and Novelty 00:04:56 Duration
Lecture 5 Churn, Responsiveness, and AB Tests 00:05:07 Duration
Lecture 6 [Quiz] Review ways to measure your recommender 00:02:56 Duration
Lecture 7 [Activity] Walkthrough of RecommenderMetrics 00:06:53 Duration
Lecture 8 [Activity] Walkthrough of TestMetrics 00:05:09 Duration
Lecture 9 [Activity] Measure the Performance of SVD Recommendations 00:02:25 Duration

Section 4 : A Recommender Engine Framework
Lecture 1 Our Recommender Engine Architecture 00:07:28 Duration
Lecture 2 [Activity] Recommender Engine Walkthrough, Part 1 00:03:55 Duration
Lecture 3 [Activity] Recommender Engine Walkthrough, Part 2 00:03:51 Duration
Lecture 4 [Activity] Review the Results of our Algorithm Evaluation 00:02:52 Duration

Section 5 : Content-Based Filtering
Lecture 1 Content-Based Recommendations, and the Cosine Similarity Metric 00:08:58 Duration
Lecture 2 K-Nearest-Neighbors and Content Recs 00:04:00 Duration
Lecture 3 [Activity] Producing and Evaluating Content-Based Movie Recommendations 00:05:24 Duration
Lecture 4 A Note on Using Implicit Ratings 00:03:36 Duration
Lecture 5 [Activity] Bleeding Edge Alert! Mise en Scene Recommendations 00:04:31 Duration
Lecture 6 [Exercise] Dive Deeper into Content-Based Recommendations 00:04:26 Duration

Section 6 : Neighborhood-Based Collaborative Filtering
Lecture 1 Measuring Similarity, and Sparsity 00:04:49 Duration
Lecture 2 Similarity Metrics 00:08:32 Duration
Lecture 3 User-based Collaborative Filtering 00:07:25 Duration
Lecture 4 [Activity] User-based Collaborative Filtering, Hands-On 00:04:53 Duration
Lecture 5 Item-based Collaborative Filtering 00:04:15 Duration
Lecture 6 [Activity] Item-based Collaborative Filtering, Hands-On 00:02:24 Duration
Lecture 7 [Exercise] Tuning Collaborative Filtering Algorithms
Lecture 8 [Activity] Evaluating Collaborative Filtering Systems Offline 00:01:29 Duration
Lecture 9 [Exercise] Measure the Hit Rate of Item-Based Collaborative Filtering 00:02:17 Duration
Lecture 10 KNN Recommenders 00:04:04 Duration
Lecture 11 [Activity] Running User and Item-Based KNN on MovieLens 00:02:26 Duration
Lecture 12 [Exercise] Experiment with different KNN parameters 00:04:26 Duration
Lecture 13 Bleeding Edge Alert! Translation-Based Recommendations 00:02:29 Duration

Section 7 : Matrix Factorization Methods
Lecture 1 Principal Component Analysis (PCA) 00:06:31 Duration
Lecture 2 Singular Value Decomposition 00:06:57 Duration
Lecture 3 [Activity] Running SVD and SVD++ on MovieLens 00:03:46 Duration
Lecture 4 Improving on SVD 00:04:34 Duration
Lecture 5 [Exercise] Tune the hyperparameters on SVD 00:01:58 Duration
Lecture 6 Bleeding Edge Alert! Sparse Linear Methods (SLIM) 00:03:30 Duration

Section 8 : Introduction to Deep Learning [Optional]
Lecture 1 Deep Learning Introduction 00:01:31 Duration
Lecture 2 Deep Learning Pre-Requisites 00:08:13 Duration
Lecture 3 History of Artificial Neural Networks 00:10:51 Duration
Lecture 4 [Activity] Playing with Tensorflow 00:12:02 Duration
Lecture 5 Training Neural Networks 00:05:47 Duration
Lecture 6 Tuning Neural Networks 00:03:53 Duration
Lecture 7 Activation Functions More Depth 00:10:36 Duration
Lecture 8 Introduction to Tensorflow 00:11:30 Duration
Lecture 9 Important Tensorflow setup note!
Lecture 10 [Activity] Handwriting Recognition with Tensorflow, part 1 00:13:19 Duration
Lecture 11 [Activity] Handwriting Recognition with Tensorflow, part 2 00:12:03 Duration
Lecture 12 Introduction to Keras 00:02:48 Duration
Lecture 13 [Activity] Handwriting Recognition with Keras 00:09:53 Duration
Lecture 14 Classifier Patterns with Keras 00:03:58 Duration
Lecture 15 [Exercise] Predict Political Parties of Politicians with Keras 00:09:55 Duration
Lecture 16 Intro to Convolutional Neural Networks (CNN's) 00:08:59 Duration
Lecture 17 CNN Architectures 00:02:54 Duration
Lecture 18 [Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs) 00:08:38 Duration
Lecture 19 Intro to Recurrent Neural Networks (RNN's) 00:07:38 Duration
Lecture 20 Training Recurrent Neural Networks 00:03:21 Duration
Lecture 21 [Activity] Sentiment Analysis of Movie Reviews using RNN's and Keras 00:11:02 Duration
Lecture 22 Tuning Neural Networks 00:04:39 Duration
Lecture 23 Neural Network Regularization Techniques 00:06:21 Duration

Section 9 : Deep Learning for Recommender Systems
Lecture 1 Intro to Deep Learning for Recommenders 00:02:19 Duration
Lecture 2 Restricted Boltzmann Machines (RBM's) 00:08:03 Duration
Lecture 3 [Activity] Recommendations with RBM's, part 1 00:12:46 Duration
Lecture 4 [Activity] Recommendations with RBM's, part 2 00:07:11 Duration
Lecture 5 [Activity] Evaluating the RBM Recommender 00:03:44 Duration
Lecture 6 [Exercise] Tuning Restricted Boltzmann Machines 00:01:43 Duration
Lecture 7 Exercise Results Tuning a RBM Recommender 00:01:15 Duration
Lecture 8 Auto-Encoders for Recommendations Deep Learning for Recs 00:04:27 Duration
Lecture 9 [Activity] Recommendations with Deep Neural Networks 00:07:23 Duration
Lecture 10 Clickstream Recommendations with RNN's 00:07:23 Duration
Lecture 11 [Exercise] Get GRU4Rec Working on your Desktop 00:02:42 Duration
Lecture 12 Exercise Results GRU4Rec in Action 00:07:51 Duration
Lecture 13 Bleeding Edge Alert! Deep Factorization Machines 00:05:49 Duration
Lecture 14 More Emerging Tech to Watch

Section 10 : Scaling it Up
Lecture 1 [Activity] Introduction and Installation of Apache Spark 00:05:49 Duration
Lecture 2 Apache Spark Architecture 00:05:13 Duration
Lecture 3 [Activity] Movie Recommendations with Spark, Matrix Factorization, and ALS 00:06:03 Duration
Lecture 4 [Activity] Recommendations from 20 million ratings with Spark 00:04:57 Duration
Lecture 5 Amazon DSSTNE 00:04:41 Duration
Lecture 6 DSSTNE in Action 00:09:25 Duration
Lecture 7 Scaling Up DSSTNE 00:02:15 Duration
Lecture 8 AWS SageMaker and Factorization Machines 00:04:24 Duration
Lecture 9 SageMaker in Action Factorization Machines on one million ratings, in the cloud 00:07:39 Duration
Lecture 10 Other Systems of Note (Amazon Personalize, RichRelevance, Recombee, and more) 00:10:30 Duration
Lecture 11 Recommender System Architecture 00:10:14 Duration

Section 11 : Real-World Challenges of Recommender Systems
Lecture 1 The Cold Start Problem (and solutions)
Lecture 2 [Exercise] Implement Random Exploration 00:00:54 Duration
Lecture 3 Exercise Solution Random Exploration 00:02:18 Duration
Lecture 4 Stoplists 00:04:48 Duration
Lecture 5 [Exercise] Implement a Stoplist 00:00:33 Duration
Lecture 6 Exercise Solution Implement a Stoplist 00:02:23 Duration
Lecture 7 Filter Bubbles, Trust, and Outliers 00:05:39 Duration
Lecture 8 [Exercise] Identify and Eliminate Outlier Users 00:00:45 Duration
Lecture 9 Exercise Solution Outlier Removal 00:04:00 Duration
Lecture 10 Fraud, The Perils of Clickstream, and International Concerns 00:04:34 Duration
Lecture 11 Temporal Effects, and Value-Aware Recommendations 00:03:31 Duration

Section 12 : Case Studies
Lecture 1 Case Study YouTube, Part 1
Lecture 2 Case Study YouTube, Part 2 00:07:04 Duration
Lecture 3 Case Study Netflix, Part 1
Lecture 4 Case Study Netflix, Part 2 00:03:56 Duration

Section 13 : Hybrid Approaches
Lecture 1 Hybrid Recommenders and Exercise 00:02:54 Duration
Lecture 2 Exercise Solution Hybrid Recommenders 00:03:39 Duration

Section 14 : Wrapping Up
Lecture 1 More to Explore 00:02:31 Duration