Natural Language Processing Fundamentals Online Course

Natural Language Processing Fundamentals Online Course

If NLP feels unfamiliar or intimidating, this course is designed to give you a strong and steady foundation. Through clear explanations and hands-on exercises, you’ll learn how to leverage Python libraries and NLP concepts to solve real-world problems effectively.

You’ll begin with an introduction to natural language processing and its practical applications, working through examples that help solidify your understanding. From there, you'll explore the early stages of problem-solving—defining the problem, gathering text data, and preparing it for modeling.

As you advance, you’ll be introduced to more complex NLP algorithms and visualization techniques that enable you to extract meaningful insights from unstructured text data. You’ll gradually move beyond foundational concepts and begin developing real-world applications, including tools that can answer user queries—such as those used in chatbots.

By the end of the course, you’ll be able to:

• Identify the appropriate type of NLP task for a given problem
• Use tools like spaCy and Gensim to perform tasks such as sentiment analysis
• Build applications that understand and respond to human language
• This course will equip you with the essential skills to move confidently from theory to building NLP-powered applications.

Who should take this Course?

The Natural Language Processing Fundamentals Online Course is ideal for data scientists, machine learning engineers, AI enthusiasts, and students who want to learn how to process, analyze, and understand human language using NLP techniques. It’s also suitable for professionals in fields like linguistics, healthcare, and customer analytics looking to apply text mining and language models. Prior knowledge of Python and basic machine learning concepts is recommended for a successful learning experience.

Course Curriculum

Introduction to NLP

• Course Overview
• Lesson Overview
• Introduction to NLP
• Various Steps in NLP – Part I
• Various Steps in NLP – Part II
• Lesson Summary

Basic Feature Extraction Methods

• Lesson Overview
• Types of Data
• Cleaning Text Data – Part I
• Cleaning Text Data – Part II
• Feature Extraction from Texts
• Feature Engineering
• Lesson Summary

Developing a Text Classifier

• Lesson Overview
• Machine Learning – Part I
• Machine Learning – Part II
• Developing a Text Classifier
• Building Pipelines for NLP Projects
• Saving and Loading Models
• Lesson Summary

Collecting Text Data from the Web

• Lesson Overview
• Collecting Data by Scraping Web Pages
• Requesting Content from Web Pages
• Dealing with Semi-Structured Data
• Lesson Summary

Topic Modeling

• Lesson Overview
• Topic Discovery
• Topic Modeling Algorithms
• Topic Fingerprinting
• Lesson Summary

Text Summarization and Text Generation

• Lesson Overview
• Automated Text Summarization
• High-Level View of Text Summarization
• TextRank
• Summarizing Text Using Different Methods
• Lesson Summary

Vector Representation

• Lesson Overview
• Vector Definition
• Encoding
• Word Embeddings and Vectors
• Lesson Summary

Sentiment Analysis

• Lesson Overview
• Sentiment Analysis
• Sentiment Analysis Tools
• TextBlob
• Understanding Data for Sentiment Analysis
• Training Sentiment Models
• Lesson Summary