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Natural Language Processing (NLP)

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Natural Language Processing (NLP)

Last Updated : July 29, 2025
F4courses
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About Course

This comprehensive course offers an in-depth
exploration of how computational systems process,
understand, and generate human language. It covers the
full spectrum of Natural Language Processing (NLP)
techniques, from foundational statistical methods like
Naive Bayes and Hidden Markov Models to cutting-edge
deep learning architectures such as Recurrent Neural
Networks (RNNs), LSTMs, and the revolutionary
Transformer models. Students will gain both a strong
theoretical understanding and practical, hands-on skills
to design, build, and rigorously evaluate sophisticated
NLP applications, including advanced text classification,
robust sentiment analysis, precise named entity
recognition, nuanced machine translation, and
innovative text generation systems.

What Will You Learn?

  • Master the fundamental linguistic challenges (e.g., ambiguity, context, syntax, semantics) and core computational approaches (rule-based, statistical, neural) in natural language processing.
  • Implement efficient text preprocessing pipelines using Python libraries like NLTK and spaCy, covering tokenization, stemming, lemmatization, stop word
  • removal, and regular expressions for diverse NLP tasks.
  • Apply classical machine learning techniques such as Naive Bayes, Support Vector Machines (SVMs), and Logistic Regression, alongside neural network
  • solutions (e.g., Convolutional Neural Networks for text, Recurrent Neural Networks for sequence data) for text classification and sentiment analysis tasks.
  • Develop robust and scalable models for advanced NLP tasks, including encoder-decoder architectures for machine translation and generative models for text summarization and conversational AI.
  • Leverage and fine-tune state-of-the-art transformer-based language models (e.g., BERT, GPT-3/4, T5) for a wide range of real-world NLP applications, understanding concepts like attention mechanisms and transfer learning.
  • Accurately evaluate the performance of diverse NLP models using industry-standard metrics such as Precision, Recall, F1-score for classification, BLEU for
  • machine translation, and ROUGE for summarization

Course Content

NLP Foundations & Text Preprocessing
Begin with an introduction to key linguistic concepts (syntax, semantics, pragmatics) relevant to NLP. Gain hands-on experience with essential text preprocessing techniques, including tokenization (word, subword), advanced stemming (Porter, Snowball) and lemmatization (WordNet Lemmatizer), efficient stop word removal, and practical application of regular expressions. Explore the inherent challenges in achieving true machine language understanding, such as lexical ambiguity, syntactic ambiguity, and anaphora resolution.

Text Representation & Embeddings
Explore traditional text representation models like Bag-of-Words, TF-IDF, and n-grams. Dive deeply into static word embeddings, comparing Word2Vec (Skip-gram, CBOW), GloVe, and FastText, and understand their limitations. Transition to contextual embeddings (e.g., ELMo, BERT embeddings) that capture meaning based on context. Learn techniques for document representation (Doc2Vec, averaged word embeddings) and methods for evaluating the quality of word and sentence embeddings, including intrinsic and extrinsic evaluations.

Text Classification & Sentiment Analysis
Examine classical machine learning approaches for text classification, such as Naive Bayes, Support Vector Machines (SVMs), and ensemble methods like Random Forests. Implement neural network solutions using Convolutional Neural Networks (CNNs) for local feature extraction and Recurrent Neural Networks (RNNs) for sequential data. Learn to fine-tune pre- trained transformer models for various classification tasks, including fine-grained sentiment analysis using sentiment lexicons and aspect-based sentiment analysis.

Named Entity Recognition & Information Extraction
Master sequence labeling with traditional statistical models like Hidden Markov Models (HMMs) and Conditional Random Fields (CRFs), then advance to neural approaches such as BiLSTM-CRF. Apply state-of-the-art transformer-based models for Named Entity Recognition (NER). Investigate relation extraction techniques, entity linking to knowledge bases (e.g., Wikidata, DBpedia), and integration with existing knowledge graphs to develop robust, end- to-end information extraction pipelines from unstructured text.

Machine Translation & Sequence-to- Sequence Models
Understand the evolution from statistical to neural machine translation (NMT), focusing on encoder- decoder architectures and the crucial role of attention mechanisms (e.g., Bahdanau and Luong attention). Explore the application of transformer- based translation models like Google Translate's Transformer. Delve into key evaluation metrics for translation quality (e.g., BLEU score) and study techniques for developing multilingual models, including zero-shot translation

Large Language Models & Advanced Applications
Conduct a deep dive into the Transformer architecture, including multi-head attention and positional encodings. Explore pre-training paradigms (e.g., masked language modeling, next sentence prediction) and efficient fine-tuning strategies. Understand few-shot and zero-shot learning, and master advanced prompt engineering techniques (e.g., chain-of-thought prompting). Discuss critical ethical considerations in large language models, including bias, fairness, transparency, and the problem of hallucination, alongside their broad applications across various domains like content generation, summarization, and question answering

Capstone Project
Students will design, implement, and rigorously evaluate an end-to-end NLP application to solve a complex, real- world problem from inception to deployment. Potential projects include developing a sophisticated sentiment analysis system for real-time customer feedback, building a specialized conversational AI chatbot for a specific domain (e.g., healthcare, finance), or creating an advanced text summarization tool capable of generating abstractive summaries from long documents. The project requires documenting the comprehensive methodology, chosen model architecture, detailed training process, and quantitative evaluation results, culminating in a technical report and presentation of findings.

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