Building Recommender Systems with Machine Learning and AI

Frank Kane, Founder of Sundog Education, ex-Amazon

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109 Lessons (8h 30m)
    • 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

      11:29
    • 56. Deep Learning Intro: Tensorflow Activity, part 1

      13:19
    • 57. Deep Learning Intro: Tensorflow Activity, part 2

      12:03
    • 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

      12:46
    • 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

      7:23
    • 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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