Natural Language Processing with Deep Dive in Python and NLTK Training Course

Course Code

python_nlp

Duration

35 hours (usually 5 days including breaks)

Requirements

There are no specific requirements needed to attend this course.

Overview

By the end of the training the delegates are expected to be sufficiently equipped with the essential python concepts and should be able to sufficiently use NLTK to implement most of the NLP and ML based operations. The training is aimed at giving not just an executional knowledge but also the logical and operational knowledge of the technology therein.

 

Course Outline

Introduction to Python

Introduction

1 - Installing Python

2 - Numbers

3 - Strings

4 - Slicing up Strings

5 - Lists

6 - Installing PyCharm

 

Conditional Statements

7 - if elif else

 

Iterations

8 - for

9 - Range and While

10 - Comments and Break

11 - Continue

 

Functions

12 - Functions

13 - Return Values

14 - Default Values for Arguments

15 - Variable Scope

16 - Keyword Arguments

17 - Flexible Number of Arguments

18 - Unpacking Arguments

19 - My trip to Walmart and Sets

20 - Dictionary

21 - Modules

 

Playing with Requests and Files

22 - Download an Image from the Web

23 - How to Read and Write Files

24 - Downloading Files from the Web

 

Exceptions

28 - Exceptions

 

Object Oriented Programs

29 - Classes and Objects

30 - init

31 - Class vs Instance Variables

32 - Inheritance

33 - Multiple Inheritance

34 - threading

 

Playing around with Python

35 - Unpack List or Tuples

36 - Zip (and yeast infection story)

37 - Lamdba

38 - Min, Max, and Sorting Dictionaries

39 - Pillow

40 - Cropping Images

41 - Combine Images Together

42 - Getting Individual Channels

43 - Awesome Merge Effect

44 - Basic Transformations

45 - Modes and Filters

46 - struct

47 - map

48 - Bitwise Operators

49 - Finding Largest or Smallest Items

50 - Dictionary Calculations

51 - Finding Most Frequent Items

52 - Dictionary Multiple Key Sort

53 - Sorting Custom Objects

 

Add Ons:

 

54 - Database Connectivity and Querying for MySQL

55 - Quick look into Regular Expressions

56 - Playing around with REST API

 

Writing a Web Crawler

 

Natural Language Processing and NLTK

Introduction to NLP (examples in Python of course)

  1. Simple Text Manipulation

    1. Searching Text

    2. Counting Words

    3. Splitting Texts into Words

    4. Lexical dispersion

  2. Processing complex structures

    1. Representing text in Lists

    2. Indexing Lists

    3. Collocations

    4. Bigrams

    5. Frequency Distributions

    6. Conditionals with Words

    7. Comparing Words (startswith, endswith, islower, isalpha, etc...)

  3. Natural Language Understanding

    1. Word Sense Disambiguation

    2. Pronoun Resolution

  4. Machine translations (statistical, rule based, literal, etc...)

  5. Exercises

NLP in Python in examples

  1. Accessing Text Corpora and Lexical Resources

    1. Common sources for corpora

    2. Conditional Frequency Distributions

    3. Counting Words by Genre

    4. Creating own corpus

    5. Pronouncing Dictionary

    6. Shoebox and Toolbox Lexicons

    7. Senses and Synonyms

    8. Hierarchies

    9. Lexical Relations: Meronyms, Holonyms

    10. Semantic Similarity

  2. Processing Raw Text

    1. Priting

    2. struncating

    3. extracting parts of string

    4. accessing individual charaters

    5. searching, replacing, spliting, joining, indexing, etc...

    6. using regular expressions

    7. detecting word patterns

    8. stemming

    9. tokenization

    10. normalization of text

    11. Word Segmentation (especially in Chinese)

  3. Categorizing and Tagging Words

    1. Tagged Corpora

    2. Tagged Tokens

    3. Part-of-Speech Tagset

    4. Python Dictionaries

    5. Words to Propertieis mapping

    6. Automatic Tagging

    7. Determining the Category of a Word (Morphological, Syntactic, Semantic)

  4. Text Classification (Machine Learning)

    1. Supervised Classification

    2. Sentence Segmentation

    3. Cross Validation

    4. Decision Trees

  5. Extracting Information from Text

    1. Chunking

    2. Chinking

    3. Tags vs Trees

  6. Analyzing Sentence Structure

    1. Context Free Grammar

    2. Parsers

  7. Building Feature Based Grammars

    1. Grammatical Features

    2. Processing Feature Structures

  8. Analyzing the Meaning of Sentences

    1. Semantics and Logic

    2. Propositional Logic

    3. First-Order Logic

    4. Discourse Semantics

  9.  Managing Linguistic Data 

    1. Data Formats (Lexicon vs Text)

    2. Metadata

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