Coursera - Natural Language Processing

[mitsumi]

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
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