Mining Massive Datasets - Stanford University [FULL COURSE]
4.0
(1)
8 learners
What you'll learn
This course includes
- 20 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 94 lessons • 20 hours of video
Mining Massive Datasets - Stanford University [FULL COURSE]
94 lessons
• 20 hours
Mining Massive Datasets - Stanford University [FULL COURSE]
94 lessons
• 20 hours
- Lecture 1 — Distributed File Systems | Stanford University 15:51
- Lecture 2 — The MapReduce Computational Model | Stanford University 22:05
- Lecture 3 — Scheduling and Data Flow | Stanford University 12:45
- Lecture 4 — Combiners and Partition Functions (Advanced) | Stanford University 12:18
- Lecture 5 — Link Analysis and PageRank | Stanford University 09:40
- Lecture 6 — PageRank The Flow Formulation | Stanford University 09:17
- Lecture 7 — PageRank The Matrix Formulation | Stanford University 08:03
- Lecture 8 — PageRank Power Iteration | Stanford University 10:35
- Lecture 9 — PageRank - The Google Formulation | Stanford University 12:09
- Lecture 10 — Why Teleports Solve the Problem | Stanford University 12:27
- Lecture 11 — How we Really Compute PageRank | Stanford University 13:50
- Lecture 12 — Finding Similar Sets | Stanford University 13:38
- Lecture 13 — Minhashing | Mining of Massive Datasets | Stanford University 25:19
- Lecture 14 — Locality Sensitive Hashing | Stanford University 19:25
- Lecture 15 — Applications of LSH | Stanford University 11:41
- Lecture 16 — Fingerprint Matching | Stanford University 07:08
- Lecture 17 — Finding Duplicate News Articles | Stanford University 06:09
- Lecture 18 — Distance Measures | Mining of Massive Datasets | Stanford University 22:40
- Lecture 19 — Nearest Neighbor Learning | Stanford University 11:40
- Lecture 20 — Frequent Itemsets | Mining of Massive Datasets | Stanford University 29:51
- Lecture 21 — A Priori Algorithm | Mining of Massive Datasets | Stanford University 13:08
- Lecture 22 — Improvements to A Priori (Advanced) | Stanford University 17:27
- Lecture 23 — All or Most Frequent Itemsets in 2 Passes (Advanced) | Stanford 14:41
- Lecture 24 — Community Detection in Graphs - Motivation | Stanford University 05:45
- Lecture 25 — The Affiliation Graph Model | Stanford University 10:05
- Lecture 26 — From AGM to BIGCLAM | Stanford University 08:49
- Lecture 27 — Solving the BIGCLAM | Mining of Massive Datasets | Stanford University 09:20
- Lecture 28 — Detecting Communities as Clusters (Advanced) | Stanford University 08:40
- Lecture 29 — What Makes a Good Cluster (Advanced) | Stanford University 08:49
- Lecture 30 — The Graph Laplacian Matrix (Advanced) | Stanford University 06:53
- Lecture 31 — Examples of Eigendecompositions of Graphs (Advanced) | Stanford 06:17
- Lecture 32 — Defining the Graph Laplacian (Advanced) | Stanford University 03:28
- Lecture 33 — Spectral Graph Partitioning Finding a Partition (Advanced) | Stanford 13:26
- Lecture 34 — Spectral Clustering Three Steps (Advanced) | Stanford University 07:18
- Lecture 35 — Analysis of Large Graphs Trawling (Advanced) | Stanford University 09:03
- Lecture 36 — Mining Data Streams | Mining of Massive Datasets | Stanford University 12:02
- Lecture 37 — Counting 1 's (Advanced) | Mining of Massive Datasets | Stanford University 29:01
- Lecture 38 — Bloom Filters | Mining of Massive Datasets | Stanford University 18:01
- Lecture 39 — Sampling a Stream | Mining of Massive Datasets | Stanford University 11:31
- Lecture 40 — Counting Distinct Elements (Advanced) | Stanford University 26:00
- Lecture 41 — Overview of Recommender Systems | Stanford University 16:52
- Lecture 42 — Content Based Recommendations | Stanford University 21:01
- Lecture 43 — Collaborative Filtering | Stanford University 20:53
- Lecture 44 — Implementing Collaborative Filtering (Advanced) | Stanford University 13:47
- Lecture 45 — Evaluating Recommender Systems | Stanford University 06:10
- Lecture 46 — Dimensionality Reduction - Introduction | Stanford University 12:02
- Lecture 47 — Singular Value Decomposition | Stanford University 13:40
- Lecture 48 — Dimensionality Reduction with SVD | Stanford University 09:05
- Lecture 49 — SVD Gives the Best Low Rank Approximation (Advanced) | Stanford 08:29
- Lecture 50 — SVD Example and Conclusion | Stanford University 11:59
- Lecture 51 — CUR Decomposition (Advanced) | Stanford University 06:28
- Lecture 52 — The CUR Algorithm (Advanced) | Stanford University 06:16
- Lecture 53 — Discussion of the CUR Method | Stanford University 07:10
- Lecture 54 — Latent Factor Models | Stanford University 16:12
- Lecture 55 — Latent Factor Recommender System | Stanford University 14:17
- Lecture 56 — Finding the Latent Factors | Stanford University 13:21
- Lecture 57 — Extension to Include Global Effects (Advanced) | Stanford University 09:43
- Lecture 58 — Overview of Clustering | Mining of Massive Datasets | Stanford University 08:47
- Lecture 59 — Hierarchical Clustering | Stanford University 14:08
- Lecture 60 — The k Means Algorithm | Stanford University 12:50
- Lecture 61 — The BFR Algorithm | Mining of Massive Datasets | Stanford University 25:02
- Lecture 62 — The CURE Algorithm (Advanced) | Stanford University 15:14
- Lecture 63 — Computational Advertising Bipartite Graph Matching | Stanford 24:48
- Lecture 64 — The AdWords Problem | Mining of Massive Datasets | Stanford University 19:22
- Lecture 65 — The Balance Algorithm | Mining of Massive Datasets | Stanford University 15:17
- Lecture 66 — Generalized Balance (Advanced) | Stanford University 14:36
- Lecture 67 — Support Vector Machines - Introduction | Stanford University 07:31
- Lecture 68 — Support Vector Machines Mathematical Formulation | Stanford 12:16
- Lecture 69 — What is the Margin | Mining of Massive Datasets | Stanford University 08:23
- Lecture 70 — Soft Margin SVMs | Mining of Massive Datasets | Stanford University 09:47
- Lecture 71 — How to Compute the Margin (Advanced) | Stanford University 14:37
- Lecture 72 — Support Vector Machines - Example | Stanford University 07:08
- Lecture 73 — Decision Trees | Mining of Massive Datasets | Stanford University 08:34
- Lecture 74 — How to Construct a Tree | Stanford University 13:22
- Lecture 75 — Information Gain | Mining of Massive Datasets | Stanford University 09:51
- Lecture 76 — Building Decision Trees Using MapReduce (Advanced ) | Stanford 08:15
- Lecture 77 — Decision Trees - Conclusion | Stanford University 07:26
- Lecture 78 — MapReduce Algorithms Part I (Advanced) | Stanford University 10:52
- Lecture 79 — MapReduce Algorithms Part II | (Advanced) | Stanford University 09:47
- Lecture 80 — Theory of MapReduce Algorithms (Advanced) | Stanford University 19:40
- Lecture 81 — Matrix Multiplication in MapReduce (Advanced) | Stanford University 24:49
- Lecture 82 — LSH Families | Mining of Massive Datasets | Stanford University 21:14
- Lecture 83 — More About LSH Families | Stanford University 12:58
- Lecture 84 — Sets and Strings With a High Degree of Similarity (Advanced) | Stanford 11:30
- Lecture 85 — Prefix of a String (Advanced) | Stanford University 07:44
- Lecture 86 — Positions Within Prefixes (Advanced) | Stanford University 14:09
- Lecture 87 — Exploiting Length (Advanced) | Stanford University 14:40
- Lecture 88 — Computing PageRank on Big Graphs (Advanced) | Stanford University 10:19
- Lecture 89 — Topic Specific PageRank | Stanford University 10:07
- Lecture 90 — Application to Measuring Proximity in Graphs | Stanford University 06:26
- Lecture 91 — Hubs and Authorities (Advanced) | Stanford University 15:17
- Lecture 92 — Web Spam - Introduction | Mining of Massive Datasets | Stanford University 06:51
- Lecture 93 — Spam Farms | Mining of Massive Datasets | Stanford University 08:01
- Lecture 94 — Trust Rank | Mining of Massive Datasets | Stanford University 10:06
