Coursera - Natural Language Processing

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.

Coursera - Machine Learning (Stanford University)

Coursera - Machine Learning (Stanford University)

Coursera - Machine Learning (Stanford University)
WEBRip | English | MP4 + PDF slides | 960 x 540 | AVC ~157 kbps | 15 fps
AAC | 128 Kbps | 44.1 KHz | 2 channels | Subs: English (.srt) | ~12 hours | 1.48 GB
Genre: eLearning Video / Artificial Neural Network, Machine Learning (ML) Algorithms

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it.