Natural Language Processing (NLP)

Data Science in Tamil > Basic > Natural Language Processing (NLP)
nlp
Why Natural Language Processing is Important
in Data Science and AI

NLP bridges the gap between human language and machines, enabling them to read, understand, and generate text and speech.

It powers essential applications like chatbots, virtual assistants, sentiment analysis, machine translation, and more.

  • NLP is critical for extracting insights from unstructured text data, which forms the majority of human communication.

  • In business and data science, NLP is used for automated document processing, feedback analysis, and trend detection.

  • Modern AI models like GPT, BERT, and T5 are built on NLP principles and Transformer architectures.

  • NLP enables voice-based interfaces and accessibility tools, expanding how users interact with software and services.

  • NLP drives innovation in healthcare (clinical notes), finance (report summarization), and law (contract analysis).

  • NLP supports search engines, recommendation systems, and even fraud detection through pattern recognition.

  • It is key to multilingual AI, making global communication seamless across languages.

  • Understanding NLP equips learners with the skills to build intelligent language-aware applications in today’s AI ecosystem.

nlp
01

Module 1: Introduction to NLP

    1. What is NLP? Definition and goals

    2. Applications of NLP in real-world systems

    3. Challenges in NLP (ambiguity, context, domain)

    4. Structured vs unstructured data

    5. Text classification vs sequence-to-sequence tasks

02

Module 2: Text Preprocessing

  1. Text normalization:

    1. Lowercasing, punctuation removal

    2. Stop word removal

  2. Tokenization (word-level, subword-level, sentence-level)

  3. Stemming vs Lemmatization

  4. Removing noise and special characters

  5. Spelling correction and slang handling

03

Module 3: Text Representation Techniques

  1. Bag of Words (BoW)

  2. Term Frequency-Inverse Document Frequency (TF-IDF)

  3. Word embeddings:

    1. Word2Vec (CBOW & Skip-gram)

    2. GloVe

    3. FastText

  4. Document embeddings and sentence vectors

04

Module 4: Syntax and Parsing

  1. Part-of-Speech (POS) tagging

  2. Named Entity Recognition (NER)

  3. Dependency parsing

  4. Constituency parsing

  5. Chunking and shallow parsing

05

Module 5: Language Modeling

  1. What is a language model?

  2. N-gram models and their limitations

  3. Perplexity and smoothing

  4. Neural language models:

    1. RNN, LSTM, GRU

    2. Transformer basics

06

Module 6: Sentiment Analysis & Text Classification

  1. Binary and multi-class sentiment classification

  2. Rule-based vs ML-based approaches

  3. Logistic regression, Naive Bayes, SVM

  4. Deep learning for classification (CNN, RNN)

  5. Evaluation metrics: accuracy, F1, precision, recall

07

Module 7: Sequence Modeling

  1. Sequence labeling: NER, POS tagging

  2. Sequence-to-sequence tasks: translation, summarization

  3. RNNs and LSTMs in sequence modeling

  4. Encoder-decoder architecture

08

Module 8: Machine Translation

  1. Rule-based and Statistical Machine Translation (SMT)

  2. Neural Machine Translation (NMT)

  3. BLEU score and evaluation metrics

  4. Transformer-based translation models

09

Module 9: Topic Modeling

  1. Latent Semantic Analysis (LSA)

  2. Latent Dirichlet Allocation (LDA)

  3. NMF (Non-negative Matrix Factorization)

  4. Visualizing and interpreting topics

  5. Use in document clustering and trend analysis

10

Module 10: Question Answering & Chatbots

  1. Types of QA systems: extractive vs generative

  2. QA datasets (SQuAD, HotpotQA)

  3. Contextual understanding using BERT

  4. Chatbot architecture:

    • Rule-based

    • Retrieval-based

    • Generative (transformer-based)

11

Module 11: Transformer Models in NLP

  1. Transformer architecture deep dive

  2. Pre-trained models:

    1. BERT, RoBERTa, DistilBERT

    2. GPT, T5, XLNet, ALBERT

  3. Fine-tuning vs feature-based approaches

  4. Hugging Face Transformers library

12

Module 12: Information Extraction & Text Mining

  1. Named Entity Recognition (NER) revisited

  2. Relation extraction

  3. Event and fact extraction

  4. Text summarization:

    1. Extractive vs abstractive

  5. Keyword extraction (RAKE, TextRank)

13

Module 13: Multilingual NLP

  1. Cross-lingual embeddings

  2. Multilingual BERT (mBERT)

  3. Translation tools and datasets

  4. Transfer learning across languages

  5. Low-resource language modeling

14

Module 14: Evaluation and Ethics in NLP

  1. Model evaluation metrics for NLP tasks

  2. Hallucination and factual consistency

  3. Bias and fairness in language models

  4. Toxicity detection and content moderation

  5. Ethical data sourcing and annotation

15

Module 15: Tools, Frameworks, and Libraries

  1. NLTK and SpaCy

  2. Gensim for topic modeling

  3. Hugging Face Transformers

  4. OpenAI and Cohere APIs

  5. LangChain for LLM-powered NLP

16

Module 16: Projects & Case Studies

  1. Sentiment analysis on real-world reviews

  2. Resume/job description matching engine

  3. Customer support chatbot using RAG

  4. Text summarizer using BERT

  5. Multilingual Q&A system

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