Awesome Natural Language Processing Tools In Python

Learn over 15+ tools including TextBlob,NLTK,Spacy,Flair, for performing NLP Projects

4.55 (56 reviews)
Udemy
platform
English
language
Data Science
category
instructor
Awesome Natural Language Processing Tools In Python
708
students
32.5 hours
content
Aug 2023
last update
$49.99
regular price

What you will learn

Understand Natural Language Processing Concepts and its implementation in code

Learn the tools for fetching data from Text Files,PDF,API,etc

Text cleaning and pre-processing for NLP projects

Stylometry in Python

Perform Sentiment Analysis with TextBlob,Vader,Flair and Machine Learning and more

Keyword Extraction using Yake,Rake,Textrank and Spacy

Build NLP Applications eg Document Redaction,Text Classification,Sentiment Analysis, Stylometry,Author Attribution,etc

Explore various tools used in an End to End NLP Project

NLP with Spacy,Flair,TextBlob,NLTK,etc

Description

Do you know that there are over 7000 human languages in the world? Is it even possible to empower machines and computers to be able to understand and process these human languages? In this course we will be exploring the concept and tools for processing human (natural) language in python.

Hence if you are interested in Natural Language Processing Projects and are curious on how sentiment analysis,text classification,summarization,and several NLP task works? Then this course is for you.


Natural Language Processing is an exciting field of Data Science but there are a lot of things to learn to keep up. New concepts and tools are emerging every day. So how do you keep up ?

In this course on Awesome Natural Language Processing Tools In Python we will take you on a journey on over 15+ tools you need to know and be aware of when doing an NLP project in a format of a workflow.

Tools and technologies are always changing but workflows and systems remain for a long time hence we will be focusing on the workflow and the tools required for each. The course approaches Natural Language Processing via the perspective of using a workflow or simple NLP Project Life Cycle.


By the end of this exciting course you will be able to

  • Fetch Textual Data From most document(docx,txt,pdf,csv),website etc

  • Clean and Preprocess unstructured text data using several tools such as NeatText,Ftfy,Regex,etc

  • Understand how tokenization works and why tokenization is important in NLP

  • Perform stylometry in python to identify and verify authors

  • NLP with Spacy,TextBlob,Flair and NLTK

  • Learn how to do text classification with Machine Learning,Transformers, TextBlob ,Flair,etc

  • Build some awesome NLP apps using Streamlit

  • Perform Sentiment Analysis From Scratch and with Several NLP Packages

  • Build features from textual data- Word2Vec,FastText,Tfidf

  • And many more


This comprehensive course focuses on not just the various tools that are useful in each step of an End to End NLP project but also how they work and how to build simple functions from scratch for your task.


Join us as we explore the world of Natural Language Processing.

See you in the Course,Stay blessed.


Tips for getting through the course

  • Please write or code along with us do not just watch,this will enhance your understanding.

  • You can regulate the speed and audio of the video as you wish,preferably at -0.75x if the speed is too fast for you.

  • Suggested Prerequisites is understanding of Python

  • This course is NOT a 'Theoretical Introduction to NLP'  nor 'Advanced Concepts in NLP' although we try our best to cover some concepts for the beginner and the pro. Rather it is about the tools used for NLP Project workflow.

Content

Introduction to Natural Language Processing

Introduction to Natural Language Processing
What is Natural Language Processing (NLP)
Applications of NLP
Most Popular NLP Libraries and Packages
NLP Project Workflow and Data Science Life Cycle
Challenges in Natural Language Processing
Ambiguity in Text and Language
Anatomy of a Text
Tools of the Craft and Installation

Module 02 - Tools For Fetching Textual Data

Fetching Textual Data - Introduction
Fetching Textual Data - Reading Text From Docx
Fetching Textual Data - Using Requests and Beautiful Soup For WebScraping
Fetching Textual Data - Webscraping Articles using NewsPaper3k
Fetching Textual Data - Working with Wikipedia
Fetching Textual Data - Fetching Multiple Articles
Fetching Textual Data - Reading Text From PDF
Fetching Textual Data - Reading Text From PDF - using pyPDF2
Fetching Textual Data - Reading Text From PDF - using PDFplumber
Fetching Textual Data - Reading Text From Txt File

Module 03 - Tools For Text Preprocessing and Text Cleaning

Text Cleaning & Text Preprocessing Workflow
Text Cleaning with NeatText -Crash Course
Text Cleaning with Pure Python using Strings
Text Cleaning & Preprocessing with Strings -Task
Tokenization - What is Tokenization
Tokenization - Why Tokenization is Important in NLP?
Tokenization - How Tokenization is Done & Types of Tokenization
Tokenization - Using Pure Python and NLTK
Tokenization - Using Spacy vs NLTK
Tokenization - Tokenizing Tweets with Casual Tokenizer
Tokenization - Sentence Tokenization
Tokenization In Tensorflow
Stemming - Stemming From Scratch
Stemming - Using Custom Logic
Stemming - Using NLTK

Module 04 - Tools For Text Analysis

Text Analysis vs NLP -Introduction
Text Analysis - Preparing the Data (Author Attribution Project)
Text Analysis - Preparing the Data ( Non Biblical Authors Data)
Text Analysis - Word Count and Word Frequency
Text Analysis - Plot of Word Frequency
Text Analysis - Plot of Word Frequency -Part 2
Text Analysis - Lexical Complexity of Text
Text Analysis - Lexical Richness and Readability
Stylometry In Python - Intro
Stylometry - Word Length Distribution and MendalHall Curve
Stylometry - Subplot For Comparing Two Authors (Author Identification)
Stylometry In Python - Author Verification

Module 04 - Building Features From Text

Building Features From Text - Introduction
How Words Are Represented In NLP
Building Features From Text - Bag of Words
Building Features From Text - One Hot Encoding
Building Features From Text - Word Count / CountVectorizer
Building Features From Text - Tools For Feature Engineering Crash Course
Word Embeddings - Gensim Word2Vec (Skipgram/CBOW) & FastText,

Natural Language Processing with TextBlob

NLP with TextBlob - Introduction and API Overview
NLP with TextBlob - Word Tokenization
NLP with TextBlob - Custom Tokenizer
NLP with TextBlob - Parts of Speech Tagging
NLP with TextBlob - Sentiment Analysis & Pure Python For Sentiment Analysis

Natural Language Processing with Flair

NLP with Flair - What is Flair & API Overview
NLP with Flair - Intro & Tokenization using Flair
NLP with Flair - Sequence Labeling, Text Annotation
NLP with Flair - Part of Speech Tagging
NLP with Flair - Named Entity Recognition
NLP with Flair - Using Multiple Taggers
NLP with Flair - Semantic Frame Detection for Sense Disambiguation
NLP with Flair - Sentiment Analysis with Flair
NLP with Flair - Text Classification with Flair

Natural Language Processing with Gensim - Topic Modeling

What is Topic Modeling?
Topic Modeling in NLP - Overview of Gensim
Topic Modeling in NLP - Workflow & Basic Terms
Topic Modeling in NLP - Introduction and Tokenization with Gensim
Topic Modeling in NLP - Gensim: Creating a Dictionary
Topic Modeling in NLP - Gensim: Creating a Bag of Words Corpus
Topic Modeling in NLP - Gensim: Using TFIDF Model

Module 04 - Text Summarization

What is Text Summarization?
Evaluating Quality of A Text Summary
Libraries For Text Summarization
Text Summarization - Extractive Summarization with Sumy
Text Summarization - Abstractive Summarization with Transformers
Evaluating Abstractive and Extractive Text Summarization using Rouge

Module 04 - Text Visualization In NLP

Text Visualization - 5 + Methods For Text Visualization

Module 04 - Text Classification

Introduction to Text Classification
Text Classification with Machine Learning Using Scikit-Learn
Multi-Label and Multi-Class Text Classification
Multi-Label Text Classification using Scikit-Multi-Learn
Text Classification with Simple Transformers - Preparing the Data
Text Classification with Simple Transformers
Text Classification with TextBlob

Module 05 - NLP Projects

Project 01 - Sentiment Analysis
Project 02 - Keyword Extraction NLP App- Demo
Project 02 - Keyword Extraction NLP App
Project 03 - Text Visualizer NLP App -Demo
Project 03 - Text Visualizer NLP App with Streamlit -Building the Structure
Project 03 - Text Visualizer NLP App with Streamlit -Adding the Functions
Project 04 - Text Analysis NLP App - Demo
Project 04 - Text Analysis NLP App with Streamlit

Reviews

Gilbert
July 9, 2022
Very useful. Clear, to the point, practical. Could not have asked for more. I learned from this course and I have been in the game for quite some time.
A.
December 29, 2021
Despite the accent of the trainer, the content is very useful. It is clear that this man knows what he is doing and what he is saying.
Vinitha
September 28, 2021
What an incredible course, structured clearly and thoughtfully with such great delivery with the instructor. Thanks Jesse. Really enjoyed learning from you, and thank you for blessing us at the end of every video - it was a warm gesture! Please make more courses!
Tej
September 17, 2021
i have watched only few videos. Overall content and coverage seems good. There is some difficulty for me to understand the English of instructor. But I am able to follow it. Only issue I find is that the frequent zoom in and zoom out. Its very irritating.
Sandip
September 13, 2021
Not complete English sentence.. its very poor teaching quality through he is having a good knowledge. So very difficult to understand what actually he trying to make student understand.
Rosario
November 9, 2020
Very good course. Extremely high value in it. Many thanks to Jesse! Strongly reccomended to everybody wants to get fast-developing skills.
John
November 6, 2020
Excellent content took a few NLP courses this has a lot of tidbits I hadn’t seen before . The accent and speed of speech I got used too after a few clips for some this might be a issue for some. But for me that’s easy to look past as the content and tools introduced gave me lots of ideas of how to change up some things I do. Not all the way through yet but already worth it. I found it has a great variety or methods and tools for some one that may have already watched a few NLP courses.

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3488904
udemy ID
9/10/2020
course created date
10/18/2020
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