Section 1 : Getting Started

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

Section 2 : Introduction to Python [Optional]

Lecture 10 [Activity] The Basics of Python 5:4
Lecture 11 Data Structures in Python 5:17
Lecture 12 Functions in Python 2:46
Lecture 13 [Exercise] Booleans, loops, and a hands-on challenge 3:52

Section 3 : Evaluating Recommender Systems

Lecture 14 TrainTest and Cross Validation 3:49
Lecture 15 Accuracy Metrics (RMSE, MAE) 4:6
Lecture 16 Top-N Hit Rate - Many Ways 4:35
Lecture 17 Coverage, Diversity, and Novelty 4:56
Lecture 18 Churn, Responsiveness, and AB Tests 5:7
Lecture 19 [Quiz] Review ways to measure your recommender 2:56
Lecture 20 [Activity] Walkthrough of RecommenderMetrics 6:53
Lecture 21 [Activity] Walkthrough of TestMetrics 5:9
Lecture 22 [Activity] Measure the Performance of SVD Recommendations 2:25

Section 4 : A Recommender Engine Framework

Lecture 23 Our Recommender Engine Architecture 7:28
Lecture 24 [Activity] Recommender Engine Walkthrough, Part 1 3:55
Lecture 25 [Activity] Recommender Engine Walkthrough, Part 2 3:51
Lecture 26 [Activity] Review the Results of our Algorithm Evaluation 2:52

Section 5 : Content-Based Filtering

Lecture 27 Content-Based Recommendations, and the Cosine Similarity Metric 8:58
Lecture 28 K-Nearest-Neighbors and Content Recs 4:0
Lecture 29 [Activity] Producing and Evaluating Content-Based Movie Recommendations 5:24
Lecture 30 A Note on Using Implicit Ratings 3:36
Lecture 31 [Activity] Bleeding Edge Alert! Mise en Scene Recommendations 4:31
Lecture 32 [Exercise] Dive Deeper into Content-Based Recommendations 4:26

Section 6 : Neighborhood-Based Collaborative Filtering

Lecture 33 Measuring Similarity, and Sparsity 4:49
Lecture 34 Similarity Metrics 8:32
Lecture 35 User-based Collaborative Filtering 7:25
Lecture 36 [Activity] User-based Collaborative Filtering, Hands-On 4:53
Lecture 37 Item-based Collaborative Filtering 4:15
Lecture 38 [Activity] Item-based Collaborative Filtering, Hands-On 2:24
Lecture 39 [Exercise] Tuning Collaborative Filtering Algorithms
Lecture 40 [Activity] Evaluating Collaborative Filtering Systems Offline 1:29
Lecture 41 [Exercise] Measure the Hit Rate of Item-Based Collaborative Filtering 2:17
Lecture 42 KNN Recommenders 4:4
Lecture 43 [Activity] Running User and Item-Based KNN on MovieLens 2:26
Lecture 44 [Exercise] Experiment with different KNN parameters 4:26
Lecture 45 Bleeding Edge Alert! Translation-Based Recommendations 2:29

Section 7 : Matrix Factorization Methods

Lecture 46 Principal Component Analysis (PCA) 6:31
Lecture 47 Singular Value Decomposition 6:57
Lecture 48 [Activity] Running SVD and SVD++ on MovieLens 3:46
Lecture 49 Improving on SVD 4:34
Lecture 50 [Exercise] Tune the hyperparameters on SVD 1:58
Lecture 51 Bleeding Edge Alert! Sparse Linear Methods (SLIM) 3:30

Section 8 : Introduction to Deep Learning [Optional]

Lecture 52 Deep Learning Introduction 1:31
Lecture 53 Deep Learning Pre-Requisites 8:13
Lecture 54 History of Artificial Neural Networks 10:51
Lecture 55 [Activity] Playing with Tensorflow 12:2
Lecture 56 Training Neural Networks 5:47
Lecture 57 Tuning Neural Networks 3:53
Lecture 58 Activation Functions More Depth 10:36
Lecture 59 Introduction to Tensorflow 11:30
Lecture 60 Important Tensorflow setup note! Text
Lecture 61 [Activity] Handwriting Recognition with Tensorflow, part 1 13:19
Lecture 62 [Activity] Handwriting Recognition with Tensorflow, part 2 12:3
Lecture 63 Introduction to Keras 2:48
Lecture 64 [Activity] Handwriting Recognition with Keras 9:53
Lecture 65 Classifier Patterns with Keras 3:58
Lecture 66 [Exercise] Predict Political Parties of Politicians with Keras 9:55
Lecture 67 Intro to Convolutional Neural Networks (CNN's) 8:59
Lecture 68 CNN Architectures 2:54
Lecture 69 [Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs) 8:38
Lecture 70 Intro to Recurrent Neural Networks (RNN's) 7:38
Lecture 71 Training Recurrent Neural Networks 3:21
Lecture 72 [Activity] Sentiment Analysis of Movie Reviews using RNN's and Keras 11:2
Lecture 73 Tuning Neural Networks 4:39
Lecture 74 Neural Network Regularization Techniques 6:21

Section 9 : Deep Learning for Recommender Systems

Lecture 79 Intro to Deep Learning for Recommenders 2:19
Lecture 80 Restricted Boltzmann Machines (RBM's) 8:3
Lecture 81 [Activity] Recommendations with RBM's, part 1 12:46
Lecture 82 [Activity] Recommendations with RBM's, part 2 7:11
Lecture 83 [Activity] Evaluating the RBM Recommender 3:44
Lecture 84 [Exercise] Tuning Restricted Boltzmann Machines 1:43
Lecture 85 Exercise Results Tuning a RBM Recommender 1:15
Lecture 86 Auto-Encoders for Recommendations Deep Learning for Recs 4:27
Lecture 87 [Activity] Recommendations with Deep Neural Networks 7:23
Lecture 88 Clickstream Recommendations with RNN's 7:23
Lecture 89 [Exercise] Get GRU4Rec Working on your Desktop 2:42
Lecture 90 Exercise Results GRU4Rec in Action 7:51
Lecture 91 Bleeding Edge Alert! Deep Factorization Machines 5:49
Lecture 92 More Emerging Tech to Watch

Section 10 : Scaling it Up

Lecture 93 [Activity] Introduction and Installation of Apache Spark 5:49
Lecture 94 Apache Spark Architecture 5:13
Lecture 95 [Activity] Movie Recommendations with Spark, Matrix Factorization, and ALS 6:3
Lecture 96 [Activity] Recommendations from 20 million ratings with Spark 4:57
Lecture 97 Amazon DSSTNE 4:41
Lecture 98 DSSTNE in Action 9:25
Lecture 99 Scaling Up DSSTNE 2:15
Lecture 100 AWS SageMaker and Factorization Machines 4:24
Lecture 101 SageMaker in Action Factorization Machines on one million ratings, in the cloud 7:39
Lecture 102 Other Systems of Note (Amazon Personalize, RichRelevance, Recombee, and more) 10:30
Lecture 103 Recommender System Architecture 10:14

Section 11 : Real-World Challenges of Recommender Systems

Lecture 104 The Cold Start Problem (and solutions)
Lecture 105 [Exercise] Implement Random Exploration 0:54
Lecture 106 Exercise Solution Random Exploration 2:18
Lecture 107 Stoplists 4:48
Lecture 108 [Exercise] Implement a Stoplist 0:33
Lecture 109 Exercise Solution Implement a Stoplist 2:23
Lecture 110 Filter Bubbles, Trust, and Outliers 5:39
Lecture 111 [Exercise] Identify and Eliminate Outlier Users 0:45
Lecture 112 Exercise Solution Outlier Removal 4:0
Lecture 113 Fraud, The Perils of Clickstream, and International Concerns 4:34
Lecture 114 Temporal Effects, and Value-Aware Recommendations 3:31

Section 12 : Case Studies

Lecture 115 Case Study YouTube, Part 1
Lecture 116 Case Study YouTube, Part 2 7:4
Lecture 117 Case Study Netflix, Part 1
Lecture 118 Case Study Netflix, Part 2 3:56

Section 13 : Hybrid Approaches

Lecture 119 Hybrid Recommenders and Exercise 2:54
Lecture 120 Exercise Solution Hybrid Recommenders 3:39

Section 14 : Wrapping Up

Lecture 121 More to Explore 2:31