Oreilly Getting Started with Natural Language Processing

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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