Building Recommender Systems with Machine Learning and AI

Video description

Help people discover new products and content with deep learning, neural networks, and machine learning recommendations.

About This Video

  • Learn how to build recommender systems from one of Amazon's pioneers in the field
  • This comprehensive course takes you all the way from the early days of collaborative filtering to bleeding-edge applications of deep neural networks and modern machine learning techniques for recommending the best items to every individual user

In Detail

Frank Kane spent over nine years at Amazon, where he managed and led the development of many of Amazon's personalized product recommendation technologies. You've seen automated recommendations everywhere—on Netflix's home page, on YouTube, and on Amazon–as these machine learning algorithms learn about your unique interests, and show the best products or content for you as an individual. These technologies have become central to the largest, most prestigious tech employers out there, and by understanding how they work, you'll become very valuable to them. We cover tried-and-true recommendation algorithms based on neighborhood-based collaborative filtering and work our way up to more modern techniques including matrix factorization and even deep learning with artificial neural networks. Along the way, you'll learn from Kane's extensive industry experience and understand the real-world challenges you'll encounter when applying these algorithms at a large scale and with real-world data. The coding exercises in this course use the Python programming language. We include an intro to Python if you're new to it, but you'll need some prior programming experience in order to use this course successfully. We also include a short introduction to deep learning if you are new to the field of artificial intelligence, but you'll need to be able to understand new computer algorithms. Hope to see you in the course soon!

Publisher resources

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Table of contents

  1. Chapter 1 : Getting Started
    1. Install Anaconda, course materials, and create movie recommendations!
    2. Course Roadmap
    3. Types of Recommenders
    4. Understanding You through Implicit and Explicit Ratings
    5. Top-N Recommender Architecture
    6. Review the basics of recommender systems.
  2. Chapter 2 : Introduction to Python
    1. The Basics of Python
    2. Data Structures in Python
    3. Functions in Python
    4. Booleans, loops, and a hands-on challenge
  3. Chapter 3 : Evaluating Recommender Systems
    1. Train/Test and Cross Validation
    2. Accuracy Metrics (RMSE, MAE)
    3. Top-N Hit Rate - Many Ways
    4. Coverage, Diversity, and Novelty
    5. Churn, Responsiveness, and A/B Tests
    6. Review ways to measure your recommender.
    7. Walkthrough of RecommenderMetrics.py
    8. Walkthrough of TestMetrics.py
    9. Measure the Performance of SVD Recommendations
  4. Chapter 4 : A Recommender Engine Framework
    1. Our Recommender Engine Architecture
    2. Recommender Engine Walkthrough, Part 1
    3. Recommender Engine Walkthrough, Part 2
    4. Review the Results of our Algorithm Evaluation.
  5. Chapter 5 : Content-Based Filtering
    1. Content-Based Recommendations, and the Cosine Similarity Metric
    2. K-Nearest-Neighbors and Content Recs
    3. Producing and Evaluating Content-Based Movie Recommendations
    4. Bleeding Edge Alert! Mise en Scene Recommendations
    5. Dive Deeper into Content-Based Recommendations
  6. Chapter 6 : Neighborhood-Based Collaborative Filtering
    1. Measuring Similarity, and Sparsity
    2. Similarity Metrics
    3. User-based Collaborative Filtering
    4. User-based Collaborative Filtering, Hands-On
    5. Item-based Collaborative Filtering
    6. Item-based Collaborative Filtering, Hands-On
    7. Tuning Collaborative Filtering Algorithms
    8. Evaluating Collaborative Filtering Systems Offline
    9. Measure the Hit Rate of Item-Based Collaborative Filtering
    10. KNN Recommenders
    11. Running User and Item-Based KNN on MovieLens
    12. Experiment with different KNN parameters.
    13. Bleeding Edge Alert! Translation-Based Recommendations
  7. Chapter 7 : Matrix Factorization Methods
    1. Principal Component Analysis (PCA)
    2. Singular Value Decomposition
    3. Running SVD and SVD++ on MovieLens
    4. Improving on SVD
    5. Tune the hyperparameters on SVD
    6. Bleeding Edge Alert! Sparse Linear Methods (SLIM)
  8. Chapter 8 : Introduction to Deep Learning
    1. Deep Learning Introduction
    2. Deep Learning Pre-Requisites
    3. History of Artificial Neural Networks
    4. [Activity] Playing with Tensorflow
    5. Training Neural Networks
    6. Tuning Neural Networks
    7. Introduction to Tensorflow
    8. [Activity] Handwriting Recognition with Tensorflow, part 1
    9. [Activity] Handwriting Recognition with Tensorflow, part 2
    10. Introduction to Keras
    11. [Activity] Handwriting Recognition with Keras
    12. Classifier Patterns with Keras
    13. [Exercise] Predict Political Parties of Politicians with Keras
    14. Intro to Convolutional Neural Networks (CNN's)
    15. CNN Architectures
    16. [Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs)
    17. Intro to Recurrent Neural Networks (RNN's)
    18. Training Recurrent Neural Networks
    19. [Activity] Sentiment Analysis of Movie Reviews using RNN's and Keras
  9. Chapter 9 : Deep Learning for Recommender Systems
    1. Intro to Deep Learning for Recommenders
    2. Restricted Boltzmann Machines (RBM's)
    3. [Activity] Recommendations with RBM's, part 1
    4. [Activity] Recommendations with RBM's, part 2
    5. [Activity] Evaluating the RBM Recommender
    6. [Exercise] Tuning Restricted Boltzmann Machines
    7. Exercise Results: Tuning a RBM Recommender
    8. Auto-Encoders for Recommendations: Deep Learning for Recs
    9. [Activity] Recommendations with Deep Neural Networks
    10. Clickstream Recommendations with RNN's
    11. [Exercise] Get GRU4Rec Working on your Desktop
    12. Exercise Results: GRU4Rec in Action
    13. Bleeding Edge Alert! Deep Factorization Machines
    14. More Emerging Tech to Watch
  10. Chapter 10 : Scaling it up
    1. [Activity] Introduction and Installation of Apache Spark
    2. Apache Spark Architecture
    3. [Activity] Movie Recommendations with Spark, Matrix Factorization, and ALS
    4. [Activity] Recommendations from 20 million ratings with Spark
    5. Amazon DSSTNE
    6. DSSTNE in Action
    7. Scaling Up DSSTNE
    8. AWS SageMaker and Factorization Machines
    9. SageMaker in Action: Factorization Machines on one million ratings, in the cloud
  11. Chapter 11 : Real-World Challenges of Recommender Systems
    1. The Cold Start Problem (and solutions)
    2. [Exercise] Implement Random Exploration
    3. Exercise Solution: Random Exploration
    4. Stoplists
    5. [Exercise] Implement a Stoplist
    6. Exercise Solution: Implement a Stoplist
    7. Filter Bubbles, Trust, and Outliers
    8. [Exercise] Identify and Eliminate Outlier Users
    9. Exercise Solution: Outlier Removal
    10. Fraud, the Perils of Clickstream, and International Concerns
    11. Temporal Effects, and Value-Aware Recommendations
  12. Chapter 12 : Case Studies
    1. Case Study: YouTube, Part 1
    2. Case Study: YouTube, Part 2
    3. Case Study: Netflix, Part 1
    4. Case Study: Netflix, Part 2
  13. Chapter 13 : Hybrid Approaches
    1. Hybrid Recommenders and Exercise
    2. Exercise Solution: Hybrid Recommenders
  14. Chapter 14 : Wrapping Up
    1. More to Explore

Product information

  • Title: Building Recommender Systems with Machine Learning and AI
  • Author(s): Frank Kane
  • Release date: January 2021
  • Publisher(s): Packt Publishing
  • ISBN: 9781789803273