2.53 GB | 00:49:55 | mp4 | 1280X720  | 16:9
Genre:eLearning |Language:English

Files Included :
001  Chapter 1  Introduction  (30.51 MB)
002  Chapter 1  Typical tasks  (131.25 MB)
003  Chapter 1  Summary  (6.7 MB)
004  Chapter 2  Your first NLP example  (24.06 MB)
005  Chapter 2  Understanding the task  (54.25 MB)
006  Chapter 2  Implementing your own spam filter  (115.06 MB)
007  Chapter 2  Deploying your spam filter in practice  (9.73 MB)
008  Chapter 2  Summary  (26.37 MB)
009  Chapter 3  Introduction to information search  (93.87 MB)
010  Chapter 3  Processing the data further  (60.78 MB)
011  Chapter 3  Information weighing  (46.85 MB)
012  Chapter 3  Practical use of the search algorithm  (50.8 MB)
013  Chapter 3  Summary  (15 MB)
014  Chapter 4  Information extraction  (41.97 MB)
015  Chapter 4  Understanding the task  (24.75 MB)
016  Chapter 4  Detecting word types with part-of-speech tagging  (88.98 MB)
017  Chapter 4  Understanding sentence structure with syntactic parsing  (46.43 MB)
018  Chapter 4  Building your own information extraction algorithm  (15.63 MB)
019  Chapter 4  Summary  (16.97 MB)
020  Chapter 5  Author profiling as a machine-learning task  (47.31 MB)
021  Chapter 5  Machine-learning pipeline at first glance  (118.41 MB)
022  Chapter 5  A closer look at the machine-learning pipeline  (82.31 MB)
023  Chapter 5  Summary  (18.9 MB)
024  Chapter 6  Linguistic feature engineering for author profiling  (39.75 MB)
025  Chapter 6  Feature engineering for authorship attribution  (141.39 MB)
026  Chapter 6  Practical use of authorship attribution and user profiling  (9.09 MB)
027  Chapter 6  Summary  (7.65 MB)
028  Chapter 7   Your first sentiment analyzer using sentiment lexicons  (40.73 MB)
029  Chapter 7  Understanding your task  (29.16 MB)
030  Chapter 7  Setting up the pipeline Data loading and analysis  (65.21 MB)
031  Chapter 7  Aggregating sentiment scores with a sentiment lexicon  (42.65 MB)
032  Chapter 7  Summary  (34.55 MB)
033  Chapter 8  Sentiment analysis with a data-driven approach  (87.38 MB)
034  Chapter 8  Addressing dependence on context with machine learning  (125.86 MB)
035  Chapter 8  Varying the length of the sentiment-bearing features  (7.21 MB)
036  Chapter 8  Negation handling for sentiment analysis  (10.93 MB)
037  Chapter 8  Further practice  (5.25 MB)
038  Chapter 8  Summary  (10.42 MB)
039  Chapter 9  Topic analysis  (138.44 MB)
040  Chapter 9  Topic discovery as an unsupervised machine-learning task  (121.53 MB)
041  Chapter 9  Summary  (34.01 MB)
042  Chapter 10  Topic modeling  (120.34 MB)
043  Chapter 10  Implementation of the topic modeling algorithm  (87.99 MB)
044  Chapter 10  Summary  (20.45 MB)
045  Chapter 11  Named-entity recognition  (49.96 MB)
046  Chapter 11  Named-entity recognition as a sequence labeling task  (88.39 MB)
047  Chapter 11  Practical applications of NER  (82.68 MB)
048  Chapter 11  Summary  (21.06 MB)
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