Building Recommender Systems with Machine Learning and AI | Frank Kane | Skillshare

Building Recommender Systems with Machine Learning and AI

Frank Kane, Founder of Sundog Education, ex-Amazon

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109 Lessons (9h 14m)
    • 1. Introduction and Setup: Build a Recommender!

      9:05
    • 2. Please Follow Me on Skillshare

      0:16
    • 3. Course Roadmap

      3:52
    • 4. Types of Recommenders

      3:22
    • 5. Understanding You through Implicit and Explicit Ratings

      4:25
    • 6. Top-N Recommender Architecture

      5:53
    • 7. Quiz

      4:46
    • 8. Intro to Python: Basics

      5:04
    • 9. Intro to Python: Data Structures

      5:17
    • 10. Intro to Python: Functions

      2:46
    • 11. Intro to Python: Booleans and Loops

      3:52
    • 12. Testing Methodologies for Recommenders

      3:49
    • 13. Measuring Prediction Accuracy

      4:06
    • 14. Measuring Hit Rate on Top-N Recommenders

      4:35
    • 15. Coverage, Diversity, and Novelty

      4:55
    • 16. Churn, Responsiveness, and A/B Tests

      5:06
    • 17. Quiz

      2:55
    • 18. Coding up recommender metrics

      6:53
    • 19. Coding up a test framework

      5:08
    • 20. Evaluating SVD recommendation results

      2:24
    • 21. Architecture of a Recommender Engine

      7:27
    • 22. Coding the Evaluator class

      3:55
    • 23. Coding the EvaluationData class

      3:51
    • 24. Reviewing the Results of our Engine

      3:10
    • 25. Content-Based Recommendations, and the Cosine Similarity Metric

      8:58
    • 26. KNN Recommenders

      3:59
    • 27. Running content-based KNN

      5:23
    • 28. Bleeding Edge Alert! Mise en Scene Recommendations

      4:31
    • 29. Exercise: Dive Deeper into Content-Based Recs

      4:26
    • 30. Measuring Similarity, and Sparsity

      4:49
    • 31. Similarity Metrics

      8:32
    • 32. User-based Collaborative Filtering

      7:25
    • 33. Implementing User-Based CF

      4:59
    • 34. Item-based Collaborative Filtering

      4:14
    • 35. Implementing Item-Based CF

      2:23
    • 36. Exercises in Collaborative Filtering

      3:31
    • 37. Evaluating Collaborative Filtering Methods

      1:28
    • 38. Exercise 2 in Collaborative Filtering

      2:17
    • 39. User-based KNN

      4:03
    • 40. Activity: KNN Recommenders

      2:25
    • 41. Exercise: KNN Recommenders

      4:25
    • 42. Bleeding Edge Alert! Translation-Based Recommendations

      2:29
    • 43. Principal Component Analysis (PCA)

      6:31
    • 44. Singular Value Decomposition

      6:56
    • 45. Activity: SVD

      3:46
    • 46. Improving on SVD

      4:33
    • 47. Exercise: SVD Recommendations

      2:02
    • 48. Bleeding Edge Alert! Sparse Linear Methods (SLIM)

      3:30
    • 49. Introduction to Deep Learning [Optional section]

      1:30
    • 50. Deep Learning Intro: Prerequisites

      8:13
    • 51. Deep Learning Intro: Artificial Neural Networks

      10:51
    • 52. Deep Learning Intro: Playing with Tensorflow

      12:02
    • 53. Deep Learning Intro: Training Neural Nets

      5:47
    • 54. Deep Learning Intro: Overfitting and Tuning

      3:55
    • 55. Deep Learning Intro: Tensorflow Introduction

      12:22
    • 56. Deep Learning Intro: Tensorflow activity, part 1

      17:45
    • 57. Deep Learning Intro: Tensorflow activity, part 2

      6:27
    • 58. Deep Learning Intro: Keras

      2:48
    • 59. Deep Learning Intro: Keras activity

      9:55
    • 60. Deep Learning Intro: Classification with Keras

      3:58
    • 61. Deep Learning Intro: Keras Exercise

      9:55
    • 62. Deep Learning Intro: Convolutional Neural Networks (CNNs)

      8:59
    • 63. Deep Learning Intro: CNN Architectures

      2:54
    • 64. Deep Learning Intro: CNN Activity

      8:38
    • 65. Deep Learning Intro: Recurrent Neural Networks (RNNs)

      7:38
    • 66. Deep Learning Intro: Training RNN's

      3:21
    • 67. Deep Learning Intro: RNN Activity

      11:01
    • 68. Recommendation Systems with Deep Learning

      2:19
    • 69. Restricted Boltzmann Machines (RBM's)

      8:02
    • 70. RBM Activity, part 1

      12:33
    • 71. RBM Activity, part 2

      7:11
    • 72. RBM Activity, part 3

      3:43
    • 73. Exercise: Tuning RBM's

      1:43
    • 74. RBM Tuning Results

      1:15
    • 75. Auto-Encoders for Recommendations: Deep Learning for Recs

      4:27
    • 76. Activity: Deep Learning on Sparse Ratings Data

      7:08
    • 77. RNN's for recommendations: GRU4Rec

      7:23
    • 78. GRU4Rec Exercise

      2:42
    • 79. Exercise Solution (GRU4Rec)

      7:51
    • 80. Bleeding Edge Alert! Deep Factorization Machines

      5:49
    • 81. More Emerging Tech to Watch

      5:14
    • 82. Introduction to Apache Spark

      5:49
    • 83. Spark Architecture

      5:13
    • 84. Movie recommendations with Spark, MLLib, and ALS

      6:02
    • 85. Scaling it up to 20 million ratings with Spark

      4:57
    • 86. Amazon DSSTNE

      4:41
    • 87. Activity: Amazon DSSTNE in action

      9:36
    • 88. Scaling up DSSTNE

      2:14
    • 89. AWS SageMaker and Factorization Machines

      4:24
    • 90. Activity: Recommendations with SageMaker

      7:38
    • 91. The Cold Start Problem (and solutions)

      6:12
    • 92. Exercise: Implement Random Exploration

      0:54
    • 93. Exercise solution

      2:18
    • 94. Stoplists

      4:48
    • 95. Exercise: Implement a Stoplist

      0:32
    • 96. Exercise solution

      2:22
    • 97. Filter Bubbles, Trust, and Outliers

      5:39
    • 98. Exercise: Remove outlier users

      0:44
    • 99. Exercise solution

      4:00
    • 100. Fraud, The Perils of Clickstream, and International Concerns

      4:33
    • 101. Temporal Effects, and Value-Aware Recommendations

      3:30
    • 102. Case Study: YouTube, Part 1

      3:42
    • 103. Case Study: YouTube, Part 2

      7:04
    • 104. Case Study: Netflix, Part 1

      3:59
    • 105. Case Study: Netflix, Part 2

      3:55
    • 106. Hybrid Recommenders and Exercise

      2:54
    • 107. Exercise solution

      4:17
    • 108. More to Explore

      2:31
    • 109. Let's Stay in Touch

      0:46
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About This Class

Learn how to build recommender systems from one of Amazon's pioneers in the field. 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'll 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 Frank's extensive industry experience to understand the real-world challenges you'll encounter when applying these algorithms at large scale and with real-world data.

This course is very hands-on; you'll develop your own framework for evaluating and combining many different recommendation algorithms together, and you'll even build your own neural networks using Tensorflow to generate recommendations from real-world movie ratings from real people. We'll cover:

  • Building a recommendation engine

  • Evaluating recommender systems

  • Content-based filtering using item attributes

  • Neighborhood-based collaborative filtering with user-based, item-based, and KNN CF

  • Model-based methods including matrix factorization and SVD

  • Applying deep learning, AI, and artificial neural networks to recommendations

  • Session-based recommendations with recursive neural networks

  • Scaling to massive data sets with Apache Spark machine learning, Amazon DSSTNE deep learning, and AWS SageMaker with factorization machines

  • Real-world challenges and solutions with recommender systems

  • Case studies from YouTube and Netflix

  • Building hybrid, ensemble recommenders

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.

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.

I hope to see you in the course soon!