Building Recommender Systems with Machine Learning and AI

Frank Kane, Founder of Sundog Education, ex-Amazon

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