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Development Programming Languages Natural Language Processing

NLTK: Build Document Classifier & Spell Checker with Python

NLP with Python - Analyzing Text with the Natural Language Toolkit (NLTK) - Natural Language Processing (NLP) Tutorial
Rating: 4.5 out of 54.5 (156 ratings)
813 students
Created by GoTrained Academy, Waqar Ahmed
Last updated 2/2019
English
English
30-Day Money-Back Guarantee

What you'll learn

  • NLTK Main Functions: Concordance, Similar, Lexical Dispersion Plot
  • Text Tokenization
  • Text Normalization: Stemming & Lemmatization
  • Text Tagging: Unigram, N-Gram, Regex
  • Text Classification
  • Project 1: Gender Prediction Application
  • Project 2: Document Classification Application
  • Information Extraction from Text: Chunking, Chinking, Name Entity Recognition
  • Source Code *.py Files of All Lectures
  • English Captions for All Lectures
  • Q&A board to send your questions and get them answered quickly

Course content

10 sections • 46 lectures • 5h 17m total length

  • Preview05:44
  • Course Technical Requirements
    01:04
  • Preview02:56
  • Preview04:26
  • Preview10:28
  • Summary: NLTK Basic Functions
    01:02
  • Frequency Distribution with NLTK
    07:50
  • Frequency Distribution on Your Text with NLTK
    04:39

  • Do you want to learn a specific NLTK or NLP topic?
    00:08

  • Accessing Corpora
    07:28
  • Loading Your Own Corpus
    06:24
  • Conditional Frequency Distribution
    13:32
  • Lexical Resources Vocabulary
    12:03
  • Terminology
    02:04
  • NLP Basic Terminology
    3 questions

  • NLP Pipeline
    04:31
  • Preview07:20
  • What is Token?
    1 question
  • Regular Expressions
    09:50
  • Applications of Regex
    09:29
  • Stemming
    07:24
  • Lemmatization
    09:27
  • Regex for Tokenization
    09:35

  • Tagger
    09:50
  • Tagged Corpus
    08:56
  • The Default Tagger
    10:33
  • Regexp Tagger
    09:13
  • Unigram Tagger
    09:00
  • Ngram Tagger
    09:56
  • POS Tagging
    5 questions

  • Machine Learning Overview
    04:51
  • Preview18:45
  • Project #1: Gender Prediction Application - Part 1
    21:46
  • Project #1: Gender Prediction Application - Part 2
    12:02
  • Project #1: Gender Prediction Application - Part 3
    14:15
  • Project #2: Document Classifier Application
    20:14

  • Information Extraction Architecture
    02:46
  • Chunking Overiew
    02:44
  • Chunking in Coding
    03:13
  • Exercise: Named Entity Recognition
    00:33
  • Chinking
    07:38
  • Stanford NLP API
    05:28
  • Chunking and Chinking
    2 questions

  • Conclusion
    01:42

  • Edit Distance Example
    02:14
  • Edit Distance - Spelling Checker
    01:45
  • Appendix: List of Correct Words for Spelling Checkers
    00:20
  • Edit Distance - Plagiarism Checker / Translation Memory
    02:01

  • More NLP Tutorials
    00:06
  • What's Next for You?
    00:22

Requirements

  • Good Python level. This Natural Language Processing (NLP) tutorial assumes that you already familiar with the basics of writing simple Python programs and that you are generally familiar with Python's core features (data structures, file handling, functions, classes, modules, common library modules, etc.).
  • Python 3.4+ (or 2.7). Please note that the tutorial codes are written in Python 3, but it is up to you to fine-tune them if you want to run them on Python 2.

Description

This Natural Language Processing (NLP) tutorial covers core basics of NLP using the well-known Python package Natural Language Toolkit (NLTK). The course helps trainees become familiar with common concepts like tokens, tokenization, stemming, lemmatization, and using regex for tokenization or for stemming. It discusses classification, tagging, normalization of our input or raw text. It also covers some machine learning algorithms such as Naive Bayes.

After taking this course, you will be familiar with the basic terminologies and concepts of Natural Language Processing (NLP) and you should be able to develop NLP applications using the knowledge you gained in this course.


What is Natural Language Processing (NLP)?

Natural language processing, or NLP for short, is the ability of a computer program to understand, manipulate, analyze, and derive meaning from human language in a smart and useful way. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, topic segmentation, and spam detection.


What is NLTK?

The Natural Language Toolkit (NLTK) is a suite of program modules and data-sets for text analysis, covering symbolic and statistical Natural Language Processing (NLP). NLTK is written in Python. Over the past few years, NLTK has become popular in teaching and research.

NLTK includes capabilities for tokenizing, parsing, and identifying named entities as well as many more features.

This Natural Language Processing (NLP) tutorial mainly cover NLTK modules.


About the course

This Natural Language Processing (NLP) tutorial is basically designed to make you understand the fundamental concepts of Natural Language Processing (NLP) with Python, and we will be learning some machine learning algorithms as well because natural language processing and machine learning move hand in hand as NLP employs machine learning techniques to learn and understand what a sentence is saying, or what a user has said and it sends an appropriate response back. 

So, by the end of this course, I hope you will have a clear idea, a clear view of the core fundamental concepts of NLP and how we can actually make applications using these core concepts.


Looking forward to seeing you in the course.


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Keywords: Natural Language Processing (NLP) tutorial; Python NLTK; Machine Learning; Sentiment Analysis; Data Mining; Text Analysis; Text Processing

Who this course is for:

  • This Natural Language Processing (NLP) tutorial is desined for Python programmers who want to learn more about ​Natural Language​ Toolkit (NLTK).

Instructors

GoTrained Academy
eLearning Professionals
GoTrained Academy
  • 4.1 Instructor Rating
  • 5,504 Reviews
  • 62,848 Students
  • 9 Courses

GoTrained is an e-learning academy aiming at creating useful content in different languages and it concentrates on technology and management.

We adopt a special approach for selecting content we provide; we mainly focus on skills that are frequently requested by clients and jobs while there are only few videos that cover them. We also try to build video series to cover not only the basics, but also the advanced areas.

Waqar Ahmed
Waqar Ahmed
  • 4.2 Instructor Rating
  • 995 Reviews
  • 6,536 Students
  • 2 Courses

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