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.