Coursera - Natural Language Processing .

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Coursera - Natural Language Processing .

Coursera - Natural Language Processing
Dan Jurafsky, Professor of Linguistics - Stanford University

WEBRip | English | MP4 + PDF Slides | 960 x 540 | AVC ~57.4 kbps | 29.970 fps
AAC | 76 Kbps | 44.1 KHz | 1 channel | Subs: English (.srt) | 17:50:24 | 1.27 GB
Genre: eLearning Video / Linguistics

This course covers a broad range of topics in natural language processing, including word and sentence tokenization, text classification and sentiment analysis, spelling correction, information extraction, parsing, meaning extraction, and question answering, We will also introduce the underlying theory from probability, statistics, and machine learning that are crucial for the field, and cover fundamental algorithms like n-gram language modeling, naive bayes and maxent classifiers, sequence models like Hidden Markov Models, probabilistic dependency and constituent parsing, and vector-space models of meaning.

We are offering this course on Natural Language Processing free and online to students worldwide, continuing Stanford's exciting forays into large scale online instruction. Students have access to screencast lecture videos, are given quiz questions, assignments and exams, receive regular feedback on progress, and can participate in a discussion forum. Those who successfully complete the course will receive a statement of accomplishment. Taught by Professors Jurafsky and Manning, the curriculum draws from Stanford's courses in Natural Language Processing. You will need a decent internet connection for accessing course materials, but should be able to watch the videos on your smartphone.

Courses list:

Week 1 - Course Introduction
Week 1 - Basic Text Processing
Week 1 - Edit Distance
Week 2 - Language Modeling
Week 2 - Spelling Correction
Week 3 - Text Classification
Week 3 - Sentiment Analysis
Week 4 - Discriminative classifiers: Maximum Entropy classifiers
Week 4 - Named entity recognition and Maximum Entropy Sequence Models
Week 4 - Relation Extraction
Week 5 - Advanced Maximum Entropy Models
Week 5 - POS Tagging
Week 5 - Parsing Introduction
Week 5 - Instructor Chat
Week 6 - Probabilistic Parsing
Week 6 - Lexicalized Parsing
Week 6 - Dependency Parsing (Optional)
Week 7 - Information Retrieval
Week 7 - Ranked Information Retrieval
Week 8 - Semantics
Week 8 - Question Answering
Week 8 - Summarization
Week 8 - Instructor Chat II

General
Complete name : 13 - 2 - Empirical_Data-Driven Approach to Parsing (7_11).mp4
Format : MPEG-4
Format profile : Base Media
Codec ID : isom (isom/iso2/avc1/mp41)
File size : 7.24 MiB
Duration : 7mn 11s
Overall bit rate mode : Variable
Overall bit rate : 141 Kbps
Encoded date : UTC 1970-01-01 00:00:00
Tagged date : UTC 1970-01-01 00:00:00
Writing application : Lavf53.29.100

Video
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Format profile : [email protected]
Format settings, CABAC : Yes
Format settings, ReFrames : 4 frames
Codec ID : avc1
Codec ID/Info : Advanced Video Coding
Duration : 7mn 10s
Bit rate : 57.4 Kbps
Width : 960 pixels
Height : 540 pixels
Display aspect ratio : 16:9
Frame rate mode : Constant
Frame rate : 29.970 (30000/1001) fps
Color space : YUV
Chroma subsampling : 4:2:0
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Scan type : Progressive
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Encoded date : UTC 1970-01-01 00:00:00
Tagged date : UTC 1970-01-01 00:00:00

Audio
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Codec ID : 40
Duration : 7mn 11s
Bit rate mode : Variable
Bit rate : 76.0 Kbps
Maximum bit rate : 128 Kbps
Channel(s) : 2 channels
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Channel positions : Front: C
Sampling rate : 44.1 KHz
Frame rate : 43.066 fps (1024 spf)
Compression mode : Lossy
Stream size : 3.90 MiB (54%)
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Alternate group : 1
Encoded date : UTC 1970-01-01 00:00:00
Tagged date : UTC 1970-01-01 00:00:00

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Coursera - Natural Language Processing .

Coursera - Natural Language Processing .

Coursera - Natural Language Processing .


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