Natural Language Processing (NLP)

About Course

Learn how computers process, analyze and understand human language. This practical course introduces text preprocessing, text representation, classification, sentiment analysis and modern NLP concepts.

Course Content

Module 01 — NLP Foundations

What is Natural Language Processing?
How Computers Process Language
NLP vs Machine Learning
Common NLP Problems
Real-World NLP Applications
NLP Development Workflow

Module 02 — Working with Text Data

Module 03 — Text Preprocessing

Module 04 — Representing Text for Machines

Module 05 — Text Classification

Module 06 — Sentiment Analysis

Module 07 — NLP Application Development

Module 08 — Modern NLP Concepts

Module 09 — Responsible NLP

Module 10 — Practical NLP Projects

Module 11 — Final NLP Project

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About Course

Course Content

Module 01 — NLP Foundations

What is Natural Language Processing?

How Computers Process Language

NLP vs Machine Learning

Common NLP Problems

Real-World NLP Applications

NLP Development Workflow

Module 02 — Working with Text Data

Understanding Text Data

Loading Text Data

Exploring Text

Text Cleaning

Lowercasing & Normalization

Removing Unwanted Characters

Module 03 — Text Preprocessing

Introduction to Tokenization

Word Tokenization

Sentence Tokenization

Stop Words

Stemming

Lemmatization

Building a Text Preprocessing Pipeline

Module 04 — Representing Text for Machines

Why Text Must Be Converted to Numbers

Bag of Words

N-Grams

TF-IDF

Introduction to Word Embeddings

Comparing Text Representations

Module 05 — Text Classification

Understanding Text Classification

Preparing Classification Data

Creating Text Features

Training a Text Classifier

Making Predictions

Evaluating Classification Results

Module 06 — Sentiment Analysis

What is Sentiment Analysis?

Preparing Sentiment Data

Positive & Negative Sentiment

Training a Sentiment Model

Predicting Sentiment

Evaluating Sentiment Results

Module 07 — NLP Application Development

Building an NLP Pipeline

Processing User Input

Connecting NLP Models to Applications

Handling Model Output

Testing NLP Applications

Module 08 — Modern NLP Concepts

Introduction to Neural NLP

Understanding Attention

Transformers Overview

Language Models Overview

Large Language Models Overview

Responsible Use of Language AI

Module 09 — Responsible NLP

Bias in Language Data

Privacy of Text Data

Understanding Model Limitations

Responsible NLP Applications

Module 10 — Practical NLP Projects

Project — Text Classification System

Project — Sentiment Analysis Application

Project — Text Processing Tool

Module 11 — Final NLP Project

Project Problem Selection

Preparing the Dataset

Text Preprocessing

Feature Engineering

Model Development

Model Evaluation

Application Integration

Final Documentation

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