Getting Started with Natural Language Processing with Python

Getting Started with Natural Language Processing with Python
Getting Started with Natural Language Processing with Python
English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 1h 43m | 244 MB

This course is all about taking raw text data and deriving insights and value from it–processing text data using standard techniques in Natural Language Processing and Machine Learning.

Text data is available in abundance on the Internet, whether it be reviews, tweets, surveys, web pages or emails. Natural language processing is a powerful skill that helps you derive immense value from that data. In this course, Getting Started with Natural Language Processing with Python, you’ll first learn about using the Natural Language Toolkit to pre-process raw text. Next, you’ll learn how to scrape websites for texting using BeautifulSoup, as well as how to auto-summarize text using machine learning. You’ll wrap up the course by exploring how to classify text using machine learning. By the end of this course you’ll be able to confidently process raw text data and apply machine learning algorithms to it.

Table of Contents

1 Course Overview
2 Recognizing Natural Language Processing Applications
3 Understanding NLP Tasks
4 Tokenizing Text
5 Removing Stopwords
6 Identifying Bigrams
7 Stemming and POS Tagging
8 Disambiguating Word Meanings
9 Contrasting Rule Based and Machine Learning Approaches
10 Understanding Types of Machine Learning Problems in NLP
11 Understanding the Mechanics of Machine Learning
12 Auto-summarizing Text Using a Rule-based Model
13 Understanding the Mechanics
14 Downloading an Article
15 Preprocessing Article Text
16 Extracting a Summary
17 Outlining the Objective
18 Building a Corpus of Tech Articles
19 Understanding the Clustering Workflow
20 Identifying Themes Using K-Means Clustering
21 Understanding the Classification Workflow
22 Assigning a Theme to an Article