Dec 26 2016

Coursera - Mining Massive Datasets (2016)

Coursera - Mining Massive Datasets (2016)

Coursera - Mining Massive Datasets (2016)
Stanford University with Jure Leskovec, Anand Rajaraman, Jeff Ullman

WEBRip | English | MP4 + PDF Guides | 960 x 540 | AVC ~76.7 kbps | 29.970 fps
AAC | 128 Kbps | 44.1 KHz | 2 channels | Subs: English (.srt) | 20:04:35 | 1.88 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 Googles PageRank algorithm models importance of Web pages and some of the many extensions that have been used for a variety of purposes.

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Well 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; well 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

also You can watch my other last:




General
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File size 13.4 MiB
Duration 8mn 48s
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Video
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Width 960 pixels
Height 540 pixels
Display aspect ratio 16:9
Frame rate mode Constant
Frame rate 29.970 fps
Color space YUV
Chroma subsampling 4:2:0
Bit depth 8 bits
Scan type Progressive
Bits/(PixelFrame) 0.005
Stream size 4.83 MiB (36%)
Writing library x264 core 138
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Language English

Audio
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Format AAC
Format/Info Advanced Audio Codec
Format profile LC
Codec ID 40
Duration 8mn 48s
Bit rate mode Constant
Bit rate 128 Kbps
Channel(s) 2 channels
Channel positions Front: L R
Sampling rate 44.1 KHz
Compression mode Lossy
Stream size 8.06 MiB (60%)
Language English

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Coursera - Mining Massive Datasets (2016)


Coursera - Mining Massive Datasets (2016)

Coursera - Mining Massive Datasets (2016)


Coursera - Mining Massive Datasets (2016)


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