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🔥 Description:
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🔥 Course content:
00:00:00 Lecture 01 — Course Introduction
00:12:50 Lecture 02 — Regular Expressions
00:24:15 Lecture 03 — Regular Expressions in Practical NLP
00:30:19 Lecture 04 — Word Tokenization
00:44:44 Lecture 05 — Word Normalization and Stemming
00:56:31 Lecture 06 — Sentence Segmentation
01:02:05 Lecture 07 — Defining Minimum Edit Distance
01:09:09 Lecture 08 — Computing Minimum Edit Distance
01:15:03 Lecture 09 — Backtrace for Computing Alignments
01:20:59 Lecture 10 — Weighted Minimum Edit Distance
01:23:46 Lecture 11 — Minimum Edit Distance in Computational Biology
01:33:15 Lecture 12 — Introduction to N-grams
01:41:56 Lecture 13 — Estimating N-gram Probabilities
01:51:33 Lecture 14 — Evaluation and Perplexity
02:02:41 Lecture 15 — Generalization and Zeros
02:07:56 Lecture 16 — Smoothing Add One
02:14:26 Lecture 17 — Interpolation
02:24:51 Lecture 18 — Good Turing Smoothing
02:40:25 Lecture 19 — Kneser Ney Smoothing
02:49:24 Lecture 20 — The Spelling Correction Task
02:55:03 Lecture 21 — The Noisy Channel Model of Spelling
03:14:33 Lecture 22 — Real Word Spelling Correction
03:23:52 Lecture 23 — State of the Art Systems
03:31:02 Lecture 24 — What is Text Classification
03:39:13 Lecture 25 — Naive Bayes
03:42:32 Lecture 26 — Formalizing the Naive Bayes Classifier
03:52:01 Lecture 27 — Naive Bayes Learning
03:57:23 Lecture 28 — Naive Bayes Relationship to Language Modeling
04:01:58 Lecture 29 — Multinomial Naive Bayes A Worked Example
04:10:57 Lecture 30 — Precision, Recall, and the F measure
04:27:12 Lecture 31 — Text Classification Evaluation
04:34:29 Lecture 32 — Practical Issues in Text Classification
04:40:25 Lecture 33 — What is Sentiment Analysis
04:47:42 Lecture 34 — Sentiment Analysis A baseline algorithm
05:01:09 Lecture 35 — Sentiment Lexicons
05:09:46 Lecture 36 — Learning Sentiment Lexicons
05:24:31 Lecture 37 — Other Sentiment Tasks
05:35:32 Lecture 38 — Generative vs Discriminative Models
05:43:21 Lecture 39 — Making features from text for discriminative NLP models
06:01:32 Lecture 40 — Feature Based Linear Classifiers
06:15:06 Lecture 41 — Building a Maxent Model The Nuts and Bolts
06:23:10 Lecture 42 — Generative vs Discriminative models The problem of
06:35:19 Lecture 43 — Introduction to Information Extraction
06:44:38 Lecture 44 — Evaluation of Named Entity Recognition
06:51:12 Lecture 45 — Sequence Models for Named Entity Recognition
07:06:17 Lecture 46 — Maximum Entropy Sequence Models
07:19:18 Lecture 47 — The Spelling Correction Task
07:24:57 Lecture 48 — The Noisy Channel Model of Spelling
07:44:26 Lecture 49 — Real Word Spelling Correction
07:53:46 Lecture 50 — State of the Art Systems
08:00:55 Lecture 51 — What is Text Classification
08:09:07 Lecture 52 — Naive Bayes
08:12:26 Lecture 53 — Formalizing the Naive Bayes Classifier
08:21:55 Lecture 54 — Naive Bayes Learning
08:27:17 Lecture 55 — Naive Bayes Relationship to Language Modeling
08:31:52 Lecture 55 — Naive Bayes Relationship to Language Modeling
08:40:50 Lecture 57 — Precision Recall and the F measure
08:57:06 Lecture 58 — Text Classification Evaluation
09:04:23 Lecture 59 — Practical Issues in Text Classification
09:10:19 Lecture 60 — What is Sentiment Analysis
09:17:36 Lecture 61 — Sentiment Analysis — A baseline algorithm
09:31:03 Lecture 62 — Sentiment Lexicons
09:39:40 Lecture 63 — Learning Sentiment Lexicons
09:54:25 Lecture 64 — Other Sentiment Tasks
10:05:26 Lecture 65 — Generative vs Discriminative Models
10:13:15 Lecture 66 — Making features from text for discriminative NLP models
10:31:26 Lecture 67 — Feature Based Linear Classifiers
10:45:00 Lecture 68 — Building a Maxent Model The Nuts and Bolts
10:53:04 Lecture 69 — Generative vs Discriminative models The problem of
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