What you'll learn
Work with text data using the Natural Language Tool Kit.
Load and manipulate custom text data.
Analyze text to discover, sentiment, important key words, and statistics.
Requirements
A computer running Windows, OS X, or Linux.
Basic Python programming knowledge.
Description
This course introduces Natural Language Processing through the use of python and the Natural Language Tool Kit. Through a practical approach, you'll get hands on experience working with and analyzing text.As a student of this course, you'll get updates for free, which include lecture revisions, new code examples, and new data projects.By the end of this course you will:Have an understanding of how to use the Natural Language Tool Kit.Be able to load and manipulate your own text data.Know how to formulate solutions to text based problems.Know when it is appropriate to apply solutions such as sentiment analysis and classification techniques.
Overview
Section 1: Course Introduction
Lecture 1 Course Intro and Outline
Section 2: Setup
Lecture 2 Windows Setup
Lecture 3 OS X Setup
Section 3: Python Refresher
Lecture 4 Lists
Lecture 5 Dictionaries
Lecture 6 Loops and Conditionals
Lecture 7 Functions
Section 4: NLTK and the Basics
Lecture 8 Overview - The Natural Language Tool Kit
Lecture 9 Counting Text
Lecture 10 Example - Words Per Sentence Trends
Lecture 11 Frequency Distribution
Lecture 12 Conditional Frequency Distribution
Lecture 13 Example - Informative Words
Lecture 14 Bigrams
Lecture 15 Overview - Regular Expressions
Lecture 16 Regular Expression Practice
Section 5: Tokenization , Tagging, Chunking
Lecture 17 Overview - Tokenization
Lecture 18 Tokenization
Lecture 19 Normalizing
Lecture 20 Part of Speech Tagging
Lecture 21 Example - Multiple Parts of Speech
Lecture 22 Example - Choices
Lecture 23 Chunking
Lecture 24 Named Entity Recognition
Section 6: Custom Sources
Lecture 25 Overview - Character Encoding
Lecture 26 Text File
Lecture 27 HTML
Lecture 28 URL
Lecture 29 CSV File
Lecture 30 Exporting
Lecture 31 NLTK Resources
Lecture 32 Example - Remove Stopwords
Section 7: Projects
Lecture 33 Sentiment Analysis Intro
Lecture 34 Basic Sentiment Analysis
Lecture 35 Gender Prediction Intro
Lecture 36 Gender Prediction
Lecture 37 TF-IDF Intro
Lecture 38 TF-IDF
Section 8: Appendix
Lecture 39 Additional NLP Resources
Lecture 40 Learning Python
Lecture 41 Future Course Content
This course is for anyone who is not familiar with Natural Language Processing and is looking for a way to start.,This course is probably not for you if you already have an understanding of Natural Language Processing and the Natural Language Tool Kit.


Homepage

https://www.udemy.com/course/natural-language-processing/

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