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Udemy Building Recommender Systems with Machine Learning and AI 2021

Video Introducing this tutorial

Getting Started :
Udemy 101: Getting the Most From This Course
[Activity] Install Anaconda, course materials, and create movie recommendations!
Course Roadmap
What Is a Recommender System?
Types of Recommenders
Understanding You through Implicit and Explicit Ratings
Top-N Recommender Architecture
[Quiz] Review the basics of recommender systems.

Introduction to Python [Optional] :
[Activity] The Basics of Python
Data Structures in Python
Functions in Python
[Exercise] Booleans, loops, and a hands-on challenge

Evaluating Recommender Systems :
Train/Test and Cross Validation
Accuracy Metrics (RMSE, MAE)
Top-N Hit Rate - Many Ways
Coverage, Diversity, and Novelty
Churn, Responsiveness, and A/B Tests
[Quiz] Review ways to measure your recommender.
[Activity] Walkthrough of
[Activity] Walkthrough of
[Activity] Measure the Performance of SVD Recommendations

A Recommender Engine Framework :
Our Recommender Engine Architecture
[Activity] Recommender Engine Walkthrough, Part 1
[Activity] Recommender Engine Walkthrough, Part 2
[Activity] Review the Results of our Algorithm Evaluation.

Content-Based Filtering :
Content-Based Recommendations, and the Cosine Similarity Metric
K-Nearest-Neighbors and Content Recs
[Activity] Producing and Evaluating Content-Based Movie Recommendations
A Note on Using Implicit Ratings.
[Activity] Bleeding Edge Alert! Mise en Scene Recommendations
[Exercise] Dive Deeper into Content-Based Recommendations

Neighborhood-Based Collaborative Filtering :
Measuring Similarity, and Sparsity
Similarity Metrics
User-based Collaborative Filtering
[Activity] User-based Collaborative Filtering, Hands-On
Item-based Collaborative Filtering
[Activity] Item-based Collaborative Filtering, Hands-On
[Exercise] Tuning Collaborative Filtering Algorithms
[Activity] Evaluating Collaborative Filtering Systems Offline
[Exercise] Measure the Hit Rate of Item-Based Collaborative Filtering
KNN Recommenders
[Activity] Running User and Item-Based KNN on MovieLens
[Exercise] Experiment with different KNN parameters.
Bleeding Edge Alert! Translation-Based Recommendations

Matrix Factorization Methods :
Principal Component Analysis (PCA)
Singular Value Decomposition
[Activity] Running SVD and SVD++ on MovieLens
Improving on SVD
[Exercise] Tune the hyperparameters on SVD
Bleeding Edge Alert! Sparse Linear Methods (SLIM)

Introduction to Deep Learning [Optional] :
Important note about Tensorflow 2
Important Tensorflow setup note!
Deep Learning Introduction
Deep Learning Pre-Requisites
History of Artificial Neural Networks
[Activity] Playing with Tensorflow
Training Neural Networks
Tuning Neural Networks
Activation Functions: More Depth
Introduction to Tensorflow
[Activity] Handwriting Recognition with Tensorflow, part 1
[Activity] Handwriting Recognition with Tensorflow, part 2
Introduction to Keras
[Activity] Handwriting Recognition with Keras
Classifier Patterns with Keras
[Exercise] Predict Political Parties of Politicians with Keras
Intro to Convolutional Neural Networks (CNN's)
CNN Architectures
[Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs)
Intro to Recurrent Neural Networks (RNN's)
Training Recurrent Neural Networks
[Activity] Sentiment Analysis of Movie Reviews using RNN's and Keras
Tuning Neural Networks
Neural Network Regularization Techniques

Deep Learning for Recommender Systems :
Intro to Deep Learning for Recommenders
Restricted Boltzmann Machines (RBM's)
[Activity] Recommendations with RBM's, part 1
[Activity] Recommendations with RBM's, part 2
[Activity] Evaluating the RBM Recommender
[Exercise] Tuning Restricted Boltzmann Machines
Exercise Results: Tuning a RBM Recommender
Auto-Encoders for Recommendations: Deep Learning for Recs
[Activity] Recommendations with Deep Neural Networks
Clickstream Recommendations with RNN's
[Exercise] Get GRU4Rec Working on your Desktop
Exercise Results: GRU4Rec in Action
Bleeding Edge Alert! Deep Factorization Machines
More Emerging Tech to Watch

Scaling it Up :
[Activity] Introduction and Installation of Apache Spark
Apache Spark Architecture
[Activity] Movie Recommendations with Spark, Matrix Factorization, and ALS
[Activity] Recommendations from 20 million ratings with Spark
DSSTNE in Action
Scaling Up DSSTNE
AWS SageMaker and Factorization Machines
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

Real-World Challenges of Recommender Systems :
The Cold Start Problem (and solutions)
[Exercise] Implement Random Exploration
Exercise Solution: Random Exploration
[Exercise] Implement a Stoplist
Exercise Solution: Implement a Stoplist
Filter Bubbles, Trust, and Outliers
[Exercise] Identify and Eliminate Outlier Users
Exercise Solution: Outlier Removal
Fraud, The Perils of Clickstream, and International Concerns
Temporal Effects, and Value-Aware Recommendations

Case Studies :
Case Study: YouTube, Part 1
Case Study: YouTube, Part 2
Case Study: Netflix, Part 1
Case Study: Netflix, Part 2

Hybrid Approaches :
Hybrid Recommenders and Exercise
Exercise Solution: Hybrid Recommenders

Wrapping Up :
More to Explore
Bonus Lecture: More courses to explore!