Coursera - Mining Massive Datasets (Stanford University)
WEBRip | English | MP4 + PDF Guides | 960 x 540 | AVC ~77 kbps | 29.970 fps
AAC | 128 Kbps | 44.1 KHz | 2 channels | Subs: English (.srt) | 20:04:35 | 2.39 GB
Genre: eLearning Video / Data Science and Big Data
We'll cover locality-sensitive hashing, a bit of magic that allows you to find similar items in a set of items so large you cannot possibly compare each pair. When data is stored as a very large, sparse matrix, dimensionality reduction is often a good way to model the data, but standard approaches do not scale well; we'll talk about efficient approaches.
Many other large-scale algorithms are covered as well, as outlined in the course syllabus.
MapReduce
Link Analysis - PageRank
Locality-Sensitive Hashing - Basics + Applications
Distance Measures
Nearest Neighbors
Frequent Itemsets
Data Stream Mining
Analysis of Large Graphs
Recommender Systems
Dimensionality Reduction
Clustering
Computational Advertising
Support-Vector Machines
Decision Trees
MapReduce Algorithms
More About Link Analysis - Topic-specific PageRank, Link Spam.
More About Locality-Sensitive Hashing
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