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:
Key Topics Covered:
removal).
word embeddings (Word2Vec, GloVe).
sentiment analysis.
and transformer models.
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:
Key Topics Covered:
removal).
word embeddings (Word2Vec, GloVe).
sentiment analysis.
and transformer models.

• Text summarizers
• Spam filters
• Sentiment analysis tools
• Question answering systems
• Chatbots
• Basics of Natural Language Processing
• Phases of NLP
• Text Preprocessing (Tokenization, Stemming, Lemmatization, Stop word Removal)
• Part-of-Speech (POS) Tagging
• Feature 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 mechanisms
• transformer 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:
• Understand the core concepts and challenges in Natural Language Processing.
• Apply text preprocessing techniques (tokenization, stemming, stopword
• Implement feature extraction methods like Bag of Words (BoW), TF-IDF, and
• Build and evaluate machine learning models for text classification and
• Work with named entity recognition (NER) and part-of-speech (POS) tagging.
• Develop language models and understand sequence modeling using RNNs, LSTMs,
• Fine-tune and use pre-trained models like BERT for downstream NLP tasks.
• Text summarizers
• Spam filters
• Sentiment analysis tools
• Question answering systems
• Chatbots
• Basics of Natural Language Processing
• Phases of NLP
• Text Preprocessing (Tokenization, Stemming, Lemmatization, Stop word Removal)
• Part-of-Speech (POS) Tagging
• Feature 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 mechanisms
• transformer 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:
• Understand the core concepts and challenges in Natural Language Processing.
• Apply text preprocessing techniques (tokenization, stemming, stopword
• Implement feature extraction methods like Bag of Words (BoW), TF-IDF, and
• Build and evaluate machine learning models for text classification and
• Work with named entity recognition (NER) and part-of-speech (POS) tagging.
• Develop language models and understand sequence modeling using RNNs, LSTMs,
• Fine-tune and use pre-trained models like BERT for downstream NLP tasks.