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NLP – Syllabus

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NLP – Syllabus

Table Of Contents:

  1. Introduction To NLP.
  2. NLP Tools & Libraries.
  3. NLP Data Formats.
  4. NLP Pipeline.
  5. Text Preprocessing Steps.
  6. Regular Expression In NLP.
  7. Embedding Techniques.
  8. Sequence Modeling.
  9. Transformers and Pre-trained Models.
  10. Evaluation Metrics
  11. Advanced NLP Tasks.
  12. Real-World NLP Projects
  13. Ethical Considerations

(1) Introduction To NLP.

  1. What is NLP ?
  2. Real-World Applications of NLP .
  3. Challenges in NLP . 
  4. Differences Between NLP, NLU, and NLG .
  5. Rule-Based vs Statistical vs Neural NLP

(2) NLP Tools & Library

  1. NLTK (Natural Language Toolkit)
  2. spaCy (Fast, production-ready NLP tasks)
  3. TextBlob (Simpler NLP tasks)
  4. Gensim (Topic modeling & word embeddings)
  5. Flair (Zalando) (Sequence labeling tasks (NER, POS))
  6. AllenNLP (Deep NLP research)
  7. FastText (Facebook) (Word embeddings & classification)
  8. Hugging Face Transformers (Pretrained models (BERT, GPT, T5, etc.))

(3) NLP Data Formats

  1. Plain Text Format (.txt)
  2. CSV / TSV / Excel Files (.csv / .tsv / .xlsx)
  3. JSON / JSONL (JSON Lines)
  4. XML Format
  5. Audio + Transcript Format (for Speech NLP)
  6. QA Datasets (e.g., SQuAD format)
  7. CONLL / IOB Format
  8. Parallel Text Format (for Machine Translation)
  9. Hugging Face Datasets Format

(4) NLP Pipe Line

  1. Text Collection / Data Ingestion
  2. Text Preprocessing
  3. Text Representation (Vectorization)
  4. Feature Engineering (Optional)
  5. Model Selection / Training
  6. Evaluation
  7. Inference / Deployment
  8. Monitoring & Feedback Loop

(5) Text Preprocessing Steps.

  1. Lowercasing
  2. Removing Noise
  3. Tokenization
  4. Stop Word Removal
  5. Stemming / Lemmatization
  6. Spelling Correction (Optional)
  7. Normalization
  8. Part-of-Speech Tagging (for NER or syntactic analysis)
  9. Named Entity Recognition (NER) (Optional, for entity-level features)
  10. Removing Rare or Frequent Words (Feature optimization)
  11. N-gram Generation (Optional)
  12. Padding / Truncation (For deep learning models)
  13. Text Vectorization

(6) Regular Expression In NLP

  1. Text Cleaning & Preprocessing
  2. Tokenization Tasks
  3. Information Extraction
  4. Text Normalization
  5. Filtering / Matching
  6. Evaluation and Diagnostics
  7. Rule-Based Classification / Labeling

(7) Embedding Techniques In NLP

(8) Sequence Modeling.

(9) Transformers and Pre-trained Models.

(10) Evaluation Metrics Used In NLP

(11) Popular NLP Benchmark

(12) Advance NLP Tasks

  1. Text Summarization
  2. Machine Translation
  3. Question Answering (QA)
  4. Sentiment Analysis
  5. Natural Language Generation (NLG)
  6. Named Entity Recognition (NER)
  7. Coreference Resolution
  8. Dialogue Systems (Conversational AI)
  9. Text Classification (Advanced)
  10. Paraphrase Detection
  11. Zero-shot Learning
  12. Multimodal NLP
  13. Text-to-Speech (TTS) & Speech-to-Text (STT)
  14. Multilingual NLP
  15. Bias and Fairness in NLP

(13) Real-World NLP Projects

  1. Sentiment Analysis for Product Reviews
  2. Chatbot for Customer Support
  3. Text Summarization for News Articles
  4. Resume Parser
  5. Fake News Detection
  6. Invoice Information Extraction
  7. Question Answering System
  8. Document Classification
  9. Text Generation / Copywriting Assistant
  10. Contract Clause Extraction and Analysis
  11. Speech-to-Text Transcription
  12. Named Entity Recognition for PII Redaction
  13. Grammar and Spell Checker
  14. Reading Comprehension
  15. Cross-lingual Search Engine

(14) Ethical Considerations

  1. Bias and Fairness
  2. Privacy and Data Security
  3. Misinformation and Disinformation
  4. Accountability and Transparency
  5. Consent and Data Ownership
  6. Toxicity and Hate Speech
  7. Digital Accessibility
  8. Environmental Impact
  9. Misuse of Technology
  10. Human-AI Collaboration

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