Building Recommender Systems with Machine Learning and AI
Frank Kane, Founder of Sundog Education, ex-Amazon
-
-
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
-