Introduction To Natural Language Processing (Nlp)

Learn how to analyze text data.

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.