Course Description

Do you know that there are over 6000 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 Tools for NLP we will take you on a journey on the various tools you need to know and be aware of when doing an NLP project in a format of a workflow.

The course approaches NLP via the perspective 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.
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.

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 withTextBlob,NLTK and Spacy
  • Learn how to do text classification with Machine Learning,Transformers, TextBlob ,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

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 basic 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.

Thanks For Enrolling


Jesse E. Agbe

Jesse has been a student of the bible since he got his first bible.He is an avid reader on optimization,productivity,technology.He is a student of ideas,beliefs and philosophies with a desire to see improvement and development in the lives of everyone.His driving goal is to help people to optimize standard technologies in solving certain problems as well as to grow their faith.Jesse loves questions and he is passionate about why things are the way they are and how to be efficient.He is a student,an author and a simple programmer.

Course curriculum

  • 2

    Module 02- Tools For Fetching Textual Data

    • Fetching Text Data- Introduction

    • Fetching Data From Docx FIle

    • Fetching Text Data using Beautifulsoup and Requests

    • Fetching Articles with NewsPaper3k

    • Fetching Text Data - Multiple Articles with Newspaper3k

    • Fetching Textual Data From Wikipedia API

    • Fetching Textual Data From PDF using Textract

    • Fetching Textual Data From PDF using PyPDF2

    • Fetching Textual Data From PDF using pdfplumber

    • Reading Data From Txt File

  • 3

    Module 03 - Tools For Text Cleaning and Text Preprocessing

    • Text Cleaning Workflow

    • Text Cleaning In Python (NLP) with NeatText

    • Text Cleaning with String Manipulation

    • Text Cleaning with String Manipulation - Applying it to a Text

  • 4

    Module 03 - Tokenization in NLP

    • What is Tokenization?

    • Why Tokenization is important in NLP?

    • How Tokenization is Done & Types of Tokens

    • Tokenization with Pure Python and NLTK

    • Word Tokenization with Spacy and Comparison with NLTK

    • Casual Tokenizer For Tweets

    • Sentence Tokenization

    • Tokenization in Tensorflow

  • 5

    Module 03 - Stemming and Lemmatization

    • Stemming with Custom Regex Function

    • Stemming with Custom Logic

    • AwesomeNLP_M03_103_Stemming in NLTK

  • 6

    Module 04 - Text Analysis and NLP

    • Text Analysis Vs NLP -Introduction

    • Text Analysis - Word Count or Frequency

    • Text Analysis - Data Preparation (Author's Dataset)

    • Text Analysis - Data Preparation 2

    • Text Analysis - Plot of Word Frequency(Most Common Tokens)

    • Text Analysis- Plot of Word Count for Jane and Shakespear

    • Text Analysis -Lexical Complexity of Text -Introduction

    • Lexical Complexity of Text In Python (Readability and Richness)

    • Text Visualization of Author's Words Using WordCloud

    • Stylometry in Python - Introduction

    • Stylometry in Python-Finding Word Length Distribution

    • Stylometry in Python -Mendelhall Curve Subplots

    • Stylometry in Python -Author Verification

  • 7

    Module 04 - Building Features From Text (Text Feature Engineering)

    • Building Features From Text

    • Representing Words in NLP

    • Representing Words In NLP using Bag of Words

    • Representing Words in NLP - One Hot Matrix using Pandas

    • Representing Words in NLP - Using BoW with Count Frequency

    • Building Features From Text - Feature Engineering of Text ( Crash Course )

    • Building Features From Text - WordEmbeddings (Word2Vec,FastText)

  • 8

    Module 04 -Tools For Text Visualization

    • Text Visualization in One Video

  • 9

    NLP with TextBlob

    • NLP with TextBlob - Intro and API Overview

    • NLP with TextBlob - Word and Sentence Tokenization

    • NLP with TextBlob -Tokenization -2

    • NLP with TextBlob - Parts of Speech Tags, Advanced Tagger

    • Text Classification with TextBlob

  • 10

    NLP with Flair

    • Natural Language Processing with Flair - What is Flair?

    • NLP with Flair - Tokenization and Working with Sentence NLP Object

    • NLP with Flair - Sequence Labeling & Text Annotation

    • NLP with Flair - Parts of Speech Tagging

    • NLP with Flair -Named Entity Recognition

    • NLP with Flair - Using Multiple Tags

    • NLP with Flair - Semantic Frame Detection & Sense Disambiguation

    • NLP with Flair - Sentiment Analysis

  • 11

    Module 05- Text Summarization In NLP

    • What is Text Summarization and Types of Summarization

    • Text Summarization - Libraries and Packages

    • Text Summarization - Extractive Summarization Using Sumy

    • Text Summarization - Graph Based -Lexrank and Textrank

    • Text Summarization - Using Gensim and Summa

    • Text Summarization - Evaluation of Text Quality

    • Text Summarization -Abstractive Summarization with Transformers

    • Evaluating Text Summary in Python

  • 12

    Module 05 - Text Classification In NLP

    • What is Text Classification?

    • Text Classification with Machine Learning

    • Multi Class vs MultiLabel Text Classification

    • Multi-label Text Classification with Scikit-MultiLearn

    • Text Classification with Simple Transformers - Data Preparation and Cleaning

    • Text Classification with Simpletransformers - (No Vectorization Needed)

    • Text Classification with Flair - PyTorch NLP Framework

  • 13

    Module 05 - Sentiment Analysis

    • Sentiment Analysis with Textblob and Custom Function From Scratch

    • Sentiment Analysis with Vader and NLTK

    • Five Markers For Checking Intensity and Valence Score

  • 14

    NLP Tools For Keyword Extraction

    • Keyword Extraction In One Video (Crash Course)

  • 15

    Module 05 - NLP Mini Projects

  • 16

    Topic Modeling in NLP

    • Topic Modeling in NLP - What is Topic Modeling

    • Topic Modeling in NLP - Gensim Overview

    • Topic Modeling with Gensim- Workflow & Basic Terms

    • Topic Modeling with Gensim - Workflow