Coursera - Mining Massive Datasets (Stanford University)

Coursera - Mining Massive Datasets (Stanford University) - Printable Version

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Coursera - Mining Massive Datasets (Stanford University) - aretr - 06-04-2019

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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 introduce the participant to modern distributed file systems and MapReduce, including what distinguishes good MapReduce algorithms from good algorithms in general. The rest of the course is devoted to algorithms for extracting models and information from large datasets. Participants will learn how Google's PageRank algorithm models importance of Web pages and some of the many extensions that have been used for a variety of purposes.

We introduce the participant to modern distributed file systems and MapReduce, including what distinguishes good MapReduce algorithms from good algorithms in general. The rest of the course is devoted to algorithms for extracting models and information from large datasets. Participants will learn how Google's PageRank algorithm models importance of Web pages and some of the many extensions that have been used for a variety of purposes.
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.
Syllabus
Week 1:
MapReduce
Link Analysis - PageRank
Week 2:
Locality-Sensitive Hashing - Basics + Applications
Distance Measures
Nearest Neighbors
Frequent Itemsets
Week 3:
Data Stream Mining
Analysis of Large Graphs
Week 4:
Recommender Systems
Dimensionality Reduction
Week 5:
Clustering
Computational Advertising
Week 6:
Support-Vector Machines
Decision Trees
MapReduce Algorithms
Week 7:
More About Link Analysis - Topic-specific PageRank, Link Spam.
More About Locality-Sensitive Hashing

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