English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 2h 21m | 1.04 GB
Natural language processing (NLP) is a subfield of computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.
Challenges in natural language processing frequently involve speech recognition, natural language understanding, and natural language generation.
Table of Contents
01 Natural Language Processing – Introduction
02 Learning objectives
03 1.1 Represent words and numbers
04 1.2 Use one-hot encoding
05 1.3 Implement bag-of-words
06 1.4 Apply stop words
07 1.5 Understand TF_IDF
08 1.6 Understand stemming
09 Learning objectives
10 2.1 Find topics in documents
11 2.2 Perform explicit semantic analysis
12 2.3 Implement document clustering
13 2.4 Implement Latent Semantic Analysis
14 2.5 Understand non-negative matrix factorization
15 Learning objectives
16 3.1 Quantify words and feelings
17 3.2 Use negations and modifiers
18 3.3 Use corpus-based approaches
19 Learning objectives
20 4.1 Understand word2vec word embeddings
21 4.2 Define GloVe
22 4.3 Apply language detection
23 Natural Language Processing – Summary
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