Awesome Natural Language Processing Tools In Python

Course Curriculum

Chapter 1: Introduction to Natural Language Processing

Lecture 1: Introduction to Natural Language Processing

Lecture 2: What is Natural Language Processing (NLP)

Lecture 3: Applications of NLP

Lecture 4: Most Popular NLP Libraries and Packages

Lecture 5: NLP Project Workflow and Data Science Life Cycle

Lecture 6: Challenges in Natural Language Processing

Lecture 7: Ambiguity in Text and Language

Lecture 8: Anatomy of a Text

Lecture 9: Tools of the Craft, Installation & Course Materials

Chapter 2: Module 02 – Tools For Fetching Textual Data

Lecture 1: Fetching Textual Data – Introduction

Lecture 2: Fetching Textual Data – Reading Text From Docx

Lecture 3: Fetching Textual Data – Using Requests and Beautiful Soup For WebScraping

Lecture 4: Fetching Textual Data – Webscraping Articles using NewsPaper3k

Lecture 5: Fetching Textual Data – Working with Wikipedia

Lecture 6: Fetching Textual Data – Fetching Multiple Articles

Lecture 7: Fetching Textual Data – Reading Text From PDF

Lecture 8: Fetching Textual Data – Reading Text From PDF – using pyPDF2

Lecture 9: Fetching Textual Data – Reading Text From PDF – using PDFplumber

Lecture 10: Fetching Textual Data – Reading Text From Txt File

Chapter 3: Module 03 – Tools For Text Preprocessing and Text Cleaning

Lecture 1: Text Cleaning & Text Preprocessing Workflow

Lecture 2: Text Cleaning with NeatText -Crash Course

Lecture 3: Text Cleaning with Pure Python using Strings

Lecture 4: Text Cleaning & Preprocessing with Strings -Task

Lecture 5: Text Cleaning & Preprocessing with Texthero

Lecture 6: Tokenization – What is Tokenization

Lecture 7: Tokenization – Why Tokenization is Important in NLP?

Lecture 8: Tokenization – How Tokenization is Done & Types of Tokenization

Lecture 9: Tokenization – Using Pure Python and NLTK

Lecture 10: Tokenization – Using Spacy vs NLTK

Lecture 11: Tokenization – Tokenizing Tweets with Casual Tokenizer

Lecture 12: Tokenization – Sentence Tokenization

Lecture 13: Tokenization In Tensorflow

Lecture 14: Stemming – Stemming From Scratch

Lecture 15: Stemming – Using Custom Logic

Lecture 16: Stemming – Using NLTK

Chapter 4: Module 04 – Tools For Text Analysis

Lecture 1: Text Analysis vs NLP -Introduction

Lecture 2: Text Analysis – Preparing the Data (Author Attribution Project)

Lecture 3: Text Analysis – Preparing the Data ( Non Biblical Authors Data)

Lecture 4: Text Analysis – Word Count and Word Frequency

Lecture 5: Text Analysis – Plot of Word Frequency

Lecture 6: Text Analysis – Plot of Word Frequency -Part 2

Lecture 7: Text Analysis – Lexical Complexity of Text

Lecture 8: Text Analysis – Lexical Richness and Readability

Lecture 9: Stylometry In Python – Intro

Lecture 10: Stylometry – Word Length Distribution and MendalHall Curve

Lecture 11: Stylometry – Subplot For Comparing Two Authors (Author Identification)

Lecture 12: Stylometry In Python – Author Verification

Chapter 5: Module 04 – Building Features From Text

Lecture 1: Building Features From Text – Introduction

Lecture 2: How Words Are Represented In NLP

Lecture 3: Building Features From Text – Bag of Words

Lecture 4: Building Features From Text – One Hot Encoding

Lecture 5: Building Features From Text – Word Count / CountVectorizer

Lecture 6: Building Features From Text – Tools For Feature Engineering Crash Course

Lecture 7: Word Embeddings – Gensim Word2Vec (Skipgram/CBOW) & FastText,

Chapter 6: Natural Language Processing with TextBlob

Lecture 1: NLP with TextBlob – Introduction and API Overview

Lecture 2: NLP with TextBlob – Word Tokenization

Lecture 3: NLP with TextBlob – Custom Tokenizer

Lecture 4: NLP with TextBlob – Parts of Speech Tagging

Lecture 5: NLP with TextBlob – Sentiment Analysis & Pure Python For Sentiment Analysis

Chapter 7: Natural Language Processing with Flair

Lecture 1: NLP with Flair – What is Flair & API Overview

Lecture 2: NLP with Flair – Intro & Tokenization using Flair

Lecture 3: NLP with Flair – Sequence Labeling, Text Annotation

Lecture 4: NLP with Flair – Part of Speech Tagging

Lecture 5: NLP with Flair – Named Entity Recognition

Lecture 6: NLP with Flair – Using Multiple Taggers

Lecture 7: NLP with Flair – Semantic Frame Detection for Sense Disambiguation

Lecture 8: NLP with Flair – Sentiment Analysis with Flair

Lecture 9: NLP with Flair – Text Classification with Flair

Chapter 8: Natural Language Processing with Gensim – Topic Modeling

Lecture 1: What is Topic Modeling?

Lecture 2: Topic Modeling in NLP – Overview of Gensim

Lecture 3: Topic Modeling in NLP – Workflow & Basic Terms

Lecture 4: Topic Modeling in NLP – Introduction and Tokenization with Gensim

Lecture 5: Topic Modeling in NLP – Gensim: Creating a Dictionary

Lecture 6: Topic Modeling in NLP – Gensim: Creating a Bag of Words Corpus

Lecture 7: Topic Modeling in NLP – Gensim: Using TFIDF Model

Lecture 8: Topic Modeling in NLP – Gensim: Using LDA Model For Identifying Topics

Chapter 9: Module 04 – Text Summarization

Lecture 1: What is Text Summarization?

Lecture 2: Evaluating Quality of A Text Summary

Lecture 3: Libraries For Text Summarization

Lecture 4: Text Summarization – Extractive Summarization with Sumy

Lecture 5: Text Summarization – Abstractive Summarization with Transformers

Lecture 6: Evaluating Abstractive and Extractive Text Summarization using Rouge

Chapter 10: Module 04 – Text Visualization In NLP

Lecture 1: Text Visualization – 5 + Methods For Text Visualization

Lecture 2: Visualizing Word Vectors with RASA's Whatlies

Chapter 11: Module 04 – Text Classification

Lecture 1: Introduction to Text Classification

Lecture 2: Text Classification with Machine Learning Using Scikit-Learn

Lecture 3: Multi-Label and Multi-Class Text Classification

Lecture 4: Multi-Label Text Classification using Scikit-Multi-Learn

Lecture 5: Text Classification with Simple Transformers – Preparing the Data