Natural Language Processing For Text Analysis With spaCy

سرفصل های دوره

Learn step-by-step Natural Language Processing (NLP) in Python using spCY! Work on practical NLP Projects!

1 - Introduction To The Course

1 - Welcome to the Course

2 - Data and Code.html

3 - Python Installation

4 - Start With Google Colaboratory Environment

5 - Google Colabs and GPU

6 - Installing Packages In Google Colab

2 - Get Started with Natural Language Processing NLP With SpaCy

7 - What Is spaCy

8 - What Is a Doc Object

9 - Extracting Information From Unstructured Text Data

10 - Splitting and Cleaning Text

11 - SpaCy Language Models

12 - Stop Words

13 - Lemmitization

14 - Putting it all together in pipelines

15 - Adding Components to Pipelines

3 - RulesBased Matching For Information Extraction

16 - Token Matcher

17 - Phrase Matcher

18 - Detect Entities With Entity Ruler

19 - Lets Locate the Phone Numbers

20 - Regex Matchers

21 - Similarity Matching

4 - Word Vectors for Linguistic Information

22 - What Is Semantic Similarity

23 - Work with word vectors in spaCy

24 - Semantic Similarity With Entities

25 - Similarity Comparison With a Keyword

26 - Using ThirdParty Word Vectors

5 - Textual Interlinkages

27 - Concept behind textual interlinkages

28 - Visualise the dependency between entities

29 - Looking for specific dependencies

6 - Practical Case Studies

30 - Mining Financial Information Using POS Tagging

31 - Visualise the Entities

32 - Extract Organisation Names

7 - Some Python Data Science Concepts to Bear In Mind

33 - What Is Pandas

34 - Basic Data Cleaning With Pandas

35 - Principles of Data Visualisation

36 - Principal Component Analysis PCATheory

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تولید کننده: Udemy-Training