🤖 Natural Language Processing: Methods, Models & Applications

📖 About This Repository

This repository documents my structured learning and implementation journey through the certification program:

"Natural Language Processing: Methods, Models and Applications"
Organized by iHUB DivyaSampark (IIT Roorkee) and Ritvij Bharat Pvt. Ltd. (RBPL).

The curriculum spans the complete NLP stack:

• Foundational Python for Data Science
• Linguistic text processing
• Classical Machine Learning for NLP
• Feature Engineering & Topic Modeling
• Transformer Architectures & Generative AI

This repository includes:

• Code notebooks
• Experiments
• Concept notes
• Model evaluations
• Mini-project implementations

🗓️ Course Details

Feature	Details
Duration	54 Hours (Jan 20 – May 30, 2026)
Mode	Online (Recorded sessions available)
Schedule	Tue–Thu–Sat (6:30 PM – 7:30 PM)
Certification	iHUB DivyaSampark, IIT Roorkee

🛠️ Tech Stack

Core Programming

• Python 3.x
• Jupyter Notebook (Anaconda)

Data Science Stack

• NumPy
• Pandas
• Matplotlib
• Seaborn

NLP Libraries

• Regular Expressions
• NLTK
• SpaCy

Machine Learning

• Scikit-Learn
• Logistic Regression
• Naive Bayes
• SVM

Evaluation Metrics

• Precision
• Recall
• F1-score
• Confusion Matrix

Advanced NLP & GenAI

• TF-IDF, BOW, DTM
• Word2Vec, GloVe
• LDA, LSA
• Transformer Architecture
• Hugging Face
• GPT, BERT, RoBERTa, DistilBERT, XLNet
• OpenAI API

📚 Curriculum Progress

🟢 Module 1 — Foundations of Python for NLP

Focus: Python mastery for NLP workflows

• Python Data Types & Control Flow
• OOP Concepts
• NumPy & Pandas
• Data Visualization

🟡 Module 2 — Text Processing & Computational Linguistics

Focus: Cleaning and structuring language

• Regex mastery
• Tokenization & POS tagging
• Dependency Parsing
• Named Entity Recognition
• N-grams & Lemmatization

🟠 Module 3 — Machine Learning for NLP

Focus: Text classification systems

• Logistic Regression
• Naive Bayes
• SVM
• Model evaluation
• Bias–Variance tradeoff

🔴 Module 4 — Advanced NLP & Feature Engineering

Focus: Vectorization & Topic Modeling

• TF-IDF
• Word2Vec & GloVe
• LDA & LSA
• Sentiment Analysis pipeline

🟣 Module 5 — Generative AI & Transformers

Focus: Building intelligent generative systems

• Transformer Architecture (Attention, FFN, Masking)
• GPT & BERT internals
• Hugging Face pipelines
• Q&A Bot using BERT
• Fine-tuning & inference strategies

🎯 My Learning Objective

Beyond certification, my goal is to:

• Develop strong architectural literacy in NLP systems
• Understand transformer internals deeply
• Bridge classical ML and modern GenAI
• Prepare for production-grade RAG & agentic systems

📜 Certification

Certification issued by:
iHUB DivyaSampark – IIT Roorkee

Certificate ID to be updated upon completion.

📬 Connect

If you're working on:

• NLP pipelines
• Transformer fine-tuning
• RAG systems
• Agentic architectures

Feel free to connect or collaborate.