Natural language processing

About this course

This course provides a comprehensive introduction to Natural Language Processing (NLP) – a field at the intersection of computer science, artificial intelligence, and linguistics that focuses on the interaction between computers and human language.

Students will learn how machines process, analyze, and understand human language through text and speech. The course covers key NLP techniques such as text preprocessing, tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, language modeling, and text classification. Through a series of projects and assignments, learners will get experience building real-world NLP applications such as:

• Text summarizers
• Spam filters
• Sentiment analysis tools
• Question answering systems

ChatbotsKey Topics Covered:

Basics of Natural Language Processing

Phases of NLPText Preprocessing (Tokenization, Stemming, Lemmatization, Stop word Removal)Part-of-Speech (POS) TaggingFeature extraction

Term frequency

Inverse document frequency

Named Entity Recognition (NER)Sentiment Analysis

Text Classification

Language Modeling (n-grams, word embeddings)recurrent neural networks

Long short term memory

Attention mechanismstransformer based models

Introduction to Deep Learning for NLP (using RNNs, LSTMs, Transformers)Practical Projects: Chatbots, Text Summarization, Machine Translation

By the end of this course, learners will be able to:

• Unders

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