Tutorialsplanet NET Udemy - Building Recommender Systems with Machine Learning and AI

1. 01 Getting Started/001 Udemy 101 Getting the Most From This Course.mp419.71MB
2. 01 Getting Started/002 [Activity] Install Anaconda course materials and create movie recommendations.mp436.83MB
3. 01 Getting Started/003 Course Roadmap.mp417.14MB
4. 01 Getting Started/004 Types of Recommenders.mp426.82MB
5. 01 Getting Started/005 Understanding You through Implicit and Explicit Ratings.mp420.72MB
6. 01 Getting Started/006 Top-N Recommender Architecture.mp437.09MB
7. 01 Getting Started/007 [Quiz] Review the basics of recommender systems..mp421.3MB
8. 02 Introduction to Python [Optional]/008 [Activity] The Basics of Python.mp443.03MB
9. 02 Introduction to Python [Optional]/009 Data Structures in Python.mp424.41MB
10. 02 Introduction to Python [Optional]/010 Functions in Python.mp412.27MB
11. 02 Introduction to Python [Optional]/011 [Exercise] Booleans loops and a hands-on challenge.mp413.86MB
12. 03 Evaluating Recommender Systems/012 TrainTest and Cross Validation.mp429.05MB
13. 03 Evaluating Recommender Systems/013 Accuracy Metrics (RMSE MAE).mp440.28MB
14. 03 Evaluating Recommender Systems/014 Top-N Hit Rate - Many Ways.mp424.53MB
15. 03 Evaluating Recommender Systems/015 Coverage Diversity and Novelty.mp413.73MB
16. 03 Evaluating Recommender Systems/016 Churn Responsiveness and AB Tests.mp460.94MB
17. 03 Evaluating Recommender Systems/017 [Quiz] Review ways to measure your recommender..mp412.83MB
18. 03 Evaluating Recommender Systems/018 [Activity] Walkthrough of RecommenderMetrics.py.mp464.3MB
19. 03 Evaluating Recommender Systems/019 [Activity] Walkthrough of TestMetrics.py.mp454.36MB
20. 03 Evaluating Recommender Systems/020 [Activity] Measure the Performance of SVD Recommendations.mp421.56MB
21. 04 A Recommender Engine Framework/021 Our Recommender Engine Architecture.mp432.73MB
22. 04 A Recommender Engine Framework/022 [Activity] Recommender Engine Walkthrough Part 1.mp437.88MB
23. 04 A Recommender Engine Framework/023 [Activity] Recommender Engine Walkthrough Part 2.mp439.59MB
24. 04 A Recommender Engine Framework/024 [Activity] Review the Results of our Algorithm Evaluation..mp434.57MB
25. 05 Content-Based Filtering/025 Content-Based Recommendations and the Cosine Similarity Metric.mp461.58MB
26. 05 Content-Based Filtering/026 K-Nearest-Neighbors and Content Recs.mp419.61MB
27. 05 Content-Based Filtering/027 [Activity] Producing and Evaluating Content-Based Movie Recommendations.mp452.36MB
28. 05 Content-Based Filtering/028 [Activity] Bleeding Edge Alert Mise en Scene Recommendations.mp446.52MB
29. 05 Content-Based Filtering/029 [Exercise] Dive Deeper into Content-Based Recommendations.mp424.11MB
30. 06 Neighborhood-Based Collaborative Filtering/030 Measuring Similarity and Sparsity.mp459.1MB
31. 06 Neighborhood-Based Collaborative Filtering/031 Similarity Metrics.mp430.68MB
32. 06 Neighborhood-Based Collaborative Filtering/032 User-based Collaborative Filtering.mp434.21MB
33. 06 Neighborhood-Based Collaborative Filtering/033 [Activity] User-based Collaborative Filtering Hands-On.mp448.65MB
34. 06 Neighborhood-Based Collaborative Filtering/034 Item-based Collaborative Filtering.mp452.23MB
35. 06 Neighborhood-Based Collaborative Filtering/035 [Activity] Item-based Collaborative Filtering Hands-On.mp426.81MB
36. 06 Neighborhood-Based Collaborative Filtering/036 [Exercise] Tuning Collaborative Filtering Algorithms.mp419.7MB
37. 06 Neighborhood-Based Collaborative Filtering/037 [Activity] Evaluating Collaborative Filtering Systems Offline.mp415.43MB
38. 06 Neighborhood-Based Collaborative Filtering/038 [Exercise] Measure the Hit Rate of Item-Based Collaborative Filtering.mp49.5MB
39. 06 Neighborhood-Based Collaborative Filtering/039 KNN Recommenders.mp424.85MB
40. 06 Neighborhood-Based Collaborative Filtering/040 [Activity] Running User and Item-Based KNN on MovieLens.mp423.76MB
41. 06 Neighborhood-Based Collaborative Filtering/041 [Exercise] Experiment with different KNN parameters..mp441.26MB
42. 06 Neighborhood-Based Collaborative Filtering/042 Bleeding Edge Alert Translation-Based Recommendations.mp421.49MB
43. 07 Matrix Factorization Methods/043 Principal Component Analysis (PCA).mp461.2MB
44. 07 Matrix Factorization Methods/044 Singular Value Decomposition.mp425.07MB
45. 07 Matrix Factorization Methods/045 [Activity] Running SVD and SVD on MovieLens.mp437.49MB
46. 07 Matrix Factorization Methods/046 Improving on SVD.mp423.07MB
47. 07 Matrix Factorization Methods/047 [Exercise] Tune the hyperparameters on SVD.mp412.46MB
48. 07 Matrix Factorization Methods/048 Bleeding Edge Alert Sparse Linear Methods (SLIM).mp426.46MB
49. 08 Introduction to Deep Learning [Optional]/049 Deep Learning Introduction.mp417.62MB
50. 08 Introduction to Deep Learning [Optional]/050 Deep Learning Pre-Requisites.mp437.05MB
51. 08 Introduction to Deep Learning [Optional]/051 History of Artificial Neural Networks.mp484.21MB
52. 08 Introduction to Deep Learning [Optional]/052 [Activity] Playing with Tensorflow.mp4145.4MB
53. 08 Introduction to Deep Learning [Optional]/053 Training Neural Networks.mp438.35MB
54. 08 Introduction to Deep Learning [Optional]/054 Tuning Neural Networks.mp431.27MB
55. 08 Introduction to Deep Learning [Optional]/055 Introduction to Tensorflow.mp492.47MB
56. 08 Introduction to Deep Learning [Optional]/056 [Activity] Handwriting Recognition with Tensorflow part 1.mp4181.85MB
57. 08 Introduction to Deep Learning [Optional]/057 [Activity] Handwriting Recognition with Tensorflow part 2.mp457.58MB
58. 08 Introduction to Deep Learning [Optional]/058 Introduction to Keras.mp416.45MB
59. 08 Introduction to Deep Learning [Optional]/059 [Activity] Handwriting Recognition with Keras.mp488.71MB
60. 08 Introduction to Deep Learning [Optional]/060 Classifier Patterns with Keras.mp424.84MB
61. 08 Introduction to Deep Learning [Optional]/061 [Exercise] Predict Political Parties of Politicians with Keras.mp4100.21MB
62. 08 Introduction to Deep Learning [Optional]/062 Intro to Convolutional Neural Networks (CNNs).mp478.19MB
63. 08 Introduction to Deep Learning [Optional]/063 CNN Architectures.mp422.54MB
64. 08 Introduction to Deep Learning [Optional]/064 [Activity] Handwriting Recognition with Convolutional Neural Networks (CNNs).mp482.28MB
65. 08 Introduction to Deep Learning [Optional]/065 Intro to Recurrent Neural Networks (RNNs).mp449.64MB
66. 08 Introduction to Deep Learning [Optional]/066 Training Recurrent Neural Networks.mp420.72MB
67. 08 Introduction to Deep Learning [Optional]/067 [Activity] Sentiment Analysis of Movie Reviews using RNNs and Keras.mp4119.78MB
68. 09 Deep Learning for Recommender Systems/068 Intro to Deep Learning for Recommenders.mp442.62MB
69. 09 Deep Learning for Recommender Systems/069 Restricted Boltzmann Machines (RBMs).mp431.67MB
70. 09 Deep Learning for Recommender Systems/070 [Activity] Recommendations with RBMs part 1.mp4144.44MB
71. 09 Deep Learning for Recommender Systems/071 [Activity] Recommendations with RBMs part 2.mp476.73MB
72. 09 Deep Learning for Recommender Systems/072 [Activity] Evaluating the RBM Recommender.mp437.67MB
73. 09 Deep Learning for Recommender Systems/073 [Exercise] Tuning Restricted Boltzmann Machines.mp433.61MB
74. 09 Deep Learning for Recommender Systems/074 Exercise Results Tuning a RBM Recommender.mp411.85MB
75. 09 Deep Learning for Recommender Systems/075 Auto-Encoders for Recommendations Deep Learning for Recs.mp426.91MB
76. 09 Deep Learning for Recommender Systems/076 [Activity] Recommendations with Deep Neural Networks.mp475.41MB
77. 09 Deep Learning for Recommender Systems/077 Clickstream Recommendations with RNNs.mp448.72MB
78. 09 Deep Learning for Recommender Systems/078 [Exercise] Get GRU4Rec Working on your Desktop.mp47.46MB
79. 09 Deep Learning for Recommender Systems/079 Exercise Results GRU4Rec in Action.mp462.65MB
80. 09 Deep Learning for Recommender Systems/080 Bleeding Edge Alert Deep Factorization Machines.mp457.36MB
81. 09 Deep Learning for Recommender Systems/081 More Emerging Tech to Watch.mp427.64MB
82. 10 Scaling it Up/082 [Activity] Introduction and Installation of Apache Spark.mp453.32MB
83. 10 Scaling it Up/083 Apache Spark Architecture.mp417.36MB
84. 10 Scaling it Up/084 [Activity] Movie Recommendations with Spark Matrix Factorization and ALS.mp455.61MB
85. 10 Scaling it Up/085 [Activity] Recommendations from 20 million ratings with Spark.mp450.67MB
86. 10 Scaling it Up/086 Amazon DSSTNE.mp442.31MB
87. 10 Scaling it Up/087 DSSTNE in Action.mp4116.66MB
88. 10 Scaling it Up/088 Scaling Up DSSTNE.mp410.44MB
89. 10 Scaling it Up/089 AWS SageMaker and Factorization Machines.mp415.58MB
90. 10 Scaling it Up/090 SageMaker in Action Factorization Machines on one million ratings in the cloud.mp468.34MB
91. 11 Real-World Challenges of Recommender Systems/091 The Cold Start Problem (and solutions).mp427.79MB
92. 11 Real-World Challenges of Recommender Systems/092 [Exercise] Implement Random Exploration.mp42.2MB
93. 11 Real-World Challenges of Recommender Systems/093 Exercise Solution Random Exploration.mp424.17MB
94. 11 Real-World Challenges of Recommender Systems/094 Stoplists.mp419.91MB
95. 11 Real-World Challenges of Recommender Systems/095 [Exercise] Implement a Stoplist.mp41.35MB
96. 11 Real-World Challenges of Recommender Systems/096 Exercise Solution Implement a Stoplist.mp426.71MB
97. 11 Real-World Challenges of Recommender Systems/097 Filter Bubbles Trust and Outliers.mp492.41MB
98. 11 Real-World Challenges of Recommender Systems/098 [Exercise] Identify and Eliminate Outlier Users.mp41.77MB
99. 11 Real-World Challenges of Recommender Systems/099 Exercise Solution Outlier Removal.mp438.49MB
100. 11 Real-World Challenges of Recommender Systems/100 Fraud The Perils of Clickstream and International Concerns.mp458.23MB
101. 11 Real-World Challenges of Recommender Systems/101 Temporal Effects and Value-Aware Recommendations.mp454.02MB
102. 12 Case Studies/102 Case Study YouTube Part 1.mp426.91MB
103. 12 Case Studies/103 Case Study YouTube Part 2.mp426.26MB
104. 12 Case Studies/104 Case Study Netflix Part 1.mp427.55MB
105. 12 Case Studies/105 Case Study Netflix Part 2.mp426.57MB
106. 13 Hybrid Approaches/106 Hybrid Recommenders and Exercise.mp418.4MB
107. 13 Hybrid Approaches/107 Exercise Solution Hybrid Recommenders.mp433.18MB
108. 14 Wrapping Up/108 More to Explore.mp438.93MB
109. 14 Wrapping Up/109 Bonus Lecture Companion Book and More Courses from Sundog Education.mp421.12MB