CourseClub NET Packtpub - Building Recommender Systems with Machine Learning and AI

1. 01.Getting Started/0101.Install Anaconda, course materials, and create movie recommendations!.mp488.13MB
2. 01.Getting Started/0102.Course Roadmap.mp469.27MB
3. 01.Getting Started/0103.Types of Recommenders.mp414.11MB
4. 01.Getting Started/0104.Understanding You through Implicit and Explicit Ratings.mp49.2MB
5. 01.Getting Started/0105.Top-N Recommender Architecture.mp415.32MB
6. 01.Getting Started/0106.Review the basics of recommender systems..mp411.16MB
7. 02.Introduction to Python/0201.The Basics of Python.mp442MB
8. 02.Introduction to Python/0202.Data Structures in Python.mp411.59MB
9. 02.Introduction to Python/0203.Functions in Python.mp45.85MB
10. 02.Introduction to Python/0204.Booleans, loops, and a hands-on challenge.mp47.33MB
11. 03.Evaluating Recommender Systems/0301.TrainTest and Cross Validation.mp423.17MB
12. 03.Evaluating Recommender Systems/0302.Accuracy Metrics (RMSE, MAE).mp446.73MB
13. 03.Evaluating Recommender Systems/0303.Top-N Hit Rate - Many Ways.mp412.16MB
14. 03.Evaluating Recommender Systems/0304.Coverage, Diversity, and Novelty.mp47.94MB
15. 03.Evaluating Recommender Systems/0305.Churn, Responsiveness, and AB Tests.mp482.68MB
16. 03.Evaluating Recommender Systems/0306.Review ways to measure your recommender..mp48.26MB
17. 03.Evaluating Recommender Systems/0307.Walkthrough of RecommenderMetrics.py.mp438.78MB
18. 03.Evaluating Recommender Systems/0308.Walkthrough of TestMetrics.py.mp425.34MB
19. 03.Evaluating Recommender Systems/0309.Measure the Performance of SVD Recommendations.mp412.05MB
20. 04.A Recommender Engine Framework/0401.Our Recommender Engine Architecture.mp418.17MB
21. 04.A Recommender Engine Framework/0402.Recommender Engine Walkthrough, Part 1.mp418.55MB
22. 04.A Recommender Engine Framework/0403.Recommender Engine Walkthrough, Part 2.mp418.57MB
23. 04.A Recommender Engine Framework/0404.Review the Results of our Algorithm Evaluation..mp414.3MB
24. 05.Content-Based Filtering/0501.Content-Based Recommendations, and the Cosine Similarity Metric.mp438.47MB
25. 05.Content-Based Filtering/0502.K-Nearest-Neighbors and Content Recs.mp411.84MB
26. 05.Content-Based Filtering/0503.Producing and Evaluating Content-Based Movie Recommendations.mp427.89MB
27. 05.Content-Based Filtering/0504.Bleeding Edge Alert! Mise en Scene Recommendations.mp433.71MB
28. 05.Content-Based Filtering/0505.Dive Deeper into Content-Based Recommendations.mp410.66MB
29. 06.Neighborhood-Based Collaborative Filtering/0601.Measuring Similarity, and Sparsity.mp469.75MB
30. 06.Neighborhood-Based Collaborative Filtering/0602.Similarity Metrics.mp415.45MB
31. 06.Neighborhood-Based Collaborative Filtering/0603.User-based Collaborative Filtering.mp419.99MB
32. 06.Neighborhood-Based Collaborative Filtering/0604.User-based Collaborative Filtering, Hands-On.mp424.56MB
33. 06.Neighborhood-Based Collaborative Filtering/0605.Item-based Collaborative Filtering.mp461.59MB
34. 06.Neighborhood-Based Collaborative Filtering/0606.Item-based Collaborative Filtering, Hands-On.mp418.12MB
35. 06.Neighborhood-Based Collaborative Filtering/0607.Tuning Collaborative Filtering Algorithms.mp410.06MB
36. 06.Neighborhood-Based Collaborative Filtering/0608.Evaluating Collaborative Filtering Systems Offline.mp410.57MB
37. 06.Neighborhood-Based Collaborative Filtering/0609.Measure the Hit Rate of Item-Based Collaborative Filtering.mp44.43MB
38. 06.Neighborhood-Based Collaborative Filtering/0610.KNN Recommenders.mp421.88MB
39. 06.Neighborhood-Based Collaborative Filtering/0611.Running User and Item-Based KNN on MovieLens.mp419.63MB
40. 06.Neighborhood-Based Collaborative Filtering/0612.Experiment with different KNN parameters..mp438.78MB
41. 06.Neighborhood-Based Collaborative Filtering/0613.Bleeding Edge Alert! Translation-Based Recommendations.mp419.64MB
42. 07.Matrix Factorization Methods/0701.Principal Component Analysis (PCA).mp464.98MB
43. 07.Matrix Factorization Methods/0702.Singular Value Decomposition.mp412.98MB
44. 07.Matrix Factorization Methods/0703.Running SVD and SVD++ on MovieLens.mp423.12MB
45. 07.Matrix Factorization Methods/0704.Improving on SVD.mp49.69MB
46. 07.Matrix Factorization Methods/0705.Tune the hyperparameters on SVD.mp48.02MB
47. 07.Matrix Factorization Methods/0706.Bleeding Edge Alert! Sparse Linear Methods (SLIM).mp421.08MB
48. 08.Introduction to Deep Learning/0801.Deep Learning Introduction.mp422.8MB
49. 08.Introduction to Deep Learning/0802.Deep Learning Pre-Requisites.mp420.12MB
50. 08.Introduction to Deep Learning/0803.History of Artificial Neural Networks.mp440.44MB
51. 08.Introduction to Deep Learning/0804.[Activity] Playing with Tensorflow.mp4116.91MB
52. 08.Introduction to Deep Learning/0805.Training Neural Networks.mp418.84MB
53. 08.Introduction to Deep Learning/0806.Tuning Neural Networks.mp413.11MB
54. 08.Introduction to Deep Learning/0807.Introduction to Tensorflow.mp443MB
55. 08.Introduction to Deep Learning/0808.[Activity] Handwriting Recognition with Tensorflow, part 1.mp492.89MB
56. 08.Introduction to Deep Learning/0809.[Activity] Handwriting Recognition with Tensorflow, part 2.mp427.4MB
57. 08.Introduction to Deep Learning/0810.Introduction to Keras.mp46.67MB
58. 08.Introduction to Deep Learning/0811.[Activity] Handwriting Recognition with Keras.mp446.94MB
59. 08.Introduction to Deep Learning/0812.Classifier Patterns with Keras.mp413.12MB
60. 08.Introduction to Deep Learning/0813.[Exercise] Predict Political Parties of Politicians with Keras.mp453.7MB
61. 08.Introduction to Deep Learning/0814.Intro to Convolutional Neural Networks (CNN_s).mp436.4MB
62. 08.Introduction to Deep Learning/0815.CNN Architectures.mp49.65MB
63. 08.Introduction to Deep Learning/0816.[Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs).mp442.41MB
64. 08.Introduction to Deep Learning/0817.Intro to Recurrent Neural Networks (RNN_s).mp422.49MB
65. 08.Introduction to Deep Learning/0818.Training Recurrent Neural Networks.mp410.1MB
66. 08.Introduction to Deep Learning/0819.[Activity] Sentiment Analysis of Movie Reviews using RNN_s and Keras.mp473.37MB
67. 09.Deep Learning for Recommender Systems/0901.Intro to Deep Learning for Recommenders.mp455.99MB
68. 09.Deep Learning for Recommender Systems/0902.Restricted Boltzmann Machines (RBM_s).mp415.93MB
69. 09.Deep Learning for Recommender Systems/0903.[Activity] Recommendations with RBM_s, part 1.mp450.52MB
70. 09.Deep Learning for Recommender Systems/0904.[Activity] Recommendations with RBM_s, part 2.mp426.41MB
71. 09.Deep Learning for Recommender Systems/0905.[Activity] Evaluating the RBM Recommender.mp419.85MB
72. 09.Deep Learning for Recommender Systems/0906.[Exercise] Tuning Restricted Boltzmann Machines.mp453.71MB
73. 09.Deep Learning for Recommender Systems/0907.Exercise Results Tuning a RBM Recommender.mp46.63MB
74. 09.Deep Learning for Recommender Systems/0908.Auto-Encoders for Recommendations Deep Learning for Recs.mp411.82MB
75. 09.Deep Learning for Recommender Systems/0909.[Activity] Recommendations with Deep Neural Networks.mp437.22MB
76. 09.Deep Learning for Recommender Systems/0910.Clickstream Recommendations with RNN_s.mp424.84MB
77. 09.Deep Learning for Recommender Systems/0911.[Exercise] Get GRU4Rec Working on your Desktop.mp43.88MB
78. 09.Deep Learning for Recommender Systems/0912.Exercise Results GRU4Rec in Action.mp441.06MB
79. 09.Deep Learning for Recommender Systems/0913.Bleeding Edge Alert! Deep Factorization Machines.mp444.31MB
80. 09.Deep Learning for Recommender Systems/0914.More Emerging Tech to Watch.mp414.16MB
81. 10.Scaling it up/1001.[Activity] Introduction and Installation of Apache Spark.mp440.04MB
82. 10.Scaling it up/1002.Apache Spark Architecture.mp49.37MB
83. 10.Scaling it up/1003.[Activity] Movie Recommendations with Spark, Matrix Factorization, and ALS.mp423.76MB
84. 10.Scaling it up/1004.[Activity] Recommendations from 20 million ratings with Spark.mp426.92MB
85. 10.Scaling it up/1005.Amazon DSSTNE.mp441.35MB
86. 10.Scaling it up/1006.DSSTNE in Action.mp461.12MB
87. 10.Scaling it up/1007.Scaling Up DSSTNE.mp44.82MB
88. 10.Scaling it up/1008.AWS SageMaker and Factorization Machines.mp47.95MB
89. 10.Scaling it up/1009.SageMaker in Action Factorization Machines on one million ratings, in the cloud.mp444.2MB
90. 11.11 Real-World Challenges of Recommender Systems/1101.The Cold Start Problem (and solutions).mp411.8MB
91. 11.11 Real-World Challenges of Recommender Systems/1102.[Exercise] Implement Random Exploration.mp41.19MB
92. 11.11 Real-World Challenges of Recommender Systems/1103.Exercise Solution Random Exploration.mp415.43MB
93. 11.11 Real-World Challenges of Recommender Systems/1104.Stoplists.mp48.67MB
94. 11.11 Real-World Challenges of Recommender Systems/1105.[Exercise] Implement a Stoplist.mp4761.82KB
95. 11.11 Real-World Challenges of Recommender Systems/1106.Exercise Solution Implement a Stoplist.mp415.07MB
96. 11.11 Real-World Challenges of Recommender Systems/1107.Filter Bubbles, Trust, and Outliers.mp421.76MB
97. 11.11 Real-World Challenges of Recommender Systems/1108.[Exercise] Identify and Eliminate Outlier Users.mp41020.31KB
98. 11.11 Real-World Challenges of Recommender Systems/1109.Exercise Solution Outlier Removal.mp416.61MB
99. 11.11 Real-World Challenges of Recommender Systems/1110.Fraud, the Perils of Clickstream, and International Concerns.mp472.79MB
100. 11.11 Real-World Challenges of Recommender Systems/1111.Temporal Effects, and Value-Aware Recommendations.mp481.63MB
101. 12.Case Studies/1201.Case Study YouTube, Part 1.mp412.79MB
102. 12.Case Studies/1202.Case Study YouTube, Part 2.mp412.47MB
103. 12.Case Studies/1203.Case Study Netflix, Part 1.mp413.85MB
104. 12.Case Studies/1204.Case Study Netflix, Part 2.mp49.84MB
105. 13.Hybrid Approaches/1301.Hybrid Recommenders and Exercise.mp48.82MB
106. 13.Hybrid Approaches/1302.Exercise Solution Hybrid Recommenders.mp420.42MB
107. 14.Wrapping Up/1401.More to Explore.mp461.91MB