Natural Langauge Processing: Methods, Models & Applications

Natural language Processing: Methods, Models & Applications
(In Association with iHUB Divyasampark IIT Roorkee)

About this Course:

In today’s data-driven and AI-powered world, Natural Language Processing (NLP) plays a critical role in enabling machines to understand, interpret, and generate human language. NLP is at the core of in-demand technologies such as search engines, chatbots, sentiment analysis, machine translation, speech-to-text systems, and intelligent document processing. Therefore, to build industry-ready skills in NLP and Generative AI, this course is designed adopting a practical and hands-on approach. It is structured to connect core theory with real-world implementation, helping learners to confidently tackle practical AI and Data problems.

Course Objectives:
•   To Build a strong foundation in Python programming by understanding core language concepts, object-oriented programming, and leveraging libraries such as NumPy, Pandas, and Matplotlib for data manipulation, analysis, and visualization.
•   To preprocess, analyze, and extract insights from unstructured text data using Regular Expressions and industry-standard NLP libraries such as SpaCy and NLTK, enabling effective text cleaning and linguistic analysis.
•  Understand and apply core machine learning concepts, including supervised and unsupervised learning, model evaluation metrics (accuracy, precision, recall, F1-score), and techniques to handle overfitting and underfitting.
•   Design and implement end-to-end NLP solutions using classical machine learning models, text feature extraction techniques, word embeddings, and topic modeling for tasks such as text classification, sentiment analysis, and emotion detection.
•   Develop industry-ready expertise in Generative AI by understanding Large Language Model architectures, transformer-based attention mechanisms, and applying tools such as GPT, BERT, RoBERTa, DistilBERT, XLNet, and Hugging Face to build, fine-tune, and deploy intelligent text-generation and question-answering systems.make them able to use suitable deep learning algorithms and techniques for solving real-world problems.

Batch Details:
Class Timings: 6:30 pm – 7:30 pm (Mon-Wed-Friday) Start Date: 20th Jan 2026
Duration: 64 Hours End Date: 30th May 2026
Mode: Online Certification: iHUB Divyasampark IIT Roorkee
Registration Closed

Course Highlights:
•   Industry-Relevant Skills.
• Hands-on based learning experience through practical projects.
• Globally accepted certification from iHUB Divyasampark IIT Roorkee
•   Full-time access to recorded lectures/PPTs/PDFs/Study Materials.
•   Session on Resume Preparation/Interview Preparation.

Course Overview:

Module 1

Basics of Python Language; Python objects with details of shell/numbers/variables etc.
Comparison operators, Range, List, Comprehension, Functions, Lambda expressions
Introduction to Object oriented programming using Python
Introduction to NumPy, Random Functions, Reshape & Arithmetic Operations
Introduction to Pandas-Data cleaning and preprocessing using Pandas
Data visualization and Graphical plotting using Matplotlib and Pandas

Module 2

Regular Expressions: Literals, Metacharacters, and escaping rules, Character classes, Ranges and negated sets.
Predefined character classes. Grouping, capturing, non-capturing, and named groups
Anchors, boundaries, and positional matching.
Spacy: Tokenization, Part of Speech (POS) Tagging, Dependency Parsing.
Lemmatization, Named Entity Recognition (NER), Vector and Cosine Similarity.
NLTK: Stop Words, Stemming, WordNet, Collocations, N-grams.

Module 3

Machine Learning Basics, introduction to supervised & unsupervised learning
Logistic Regression, Sigmoid Function, Maximum Likelihood
Confusion Matrix, interpreting parameters like F-1 score, Accuracy, Precision, Recall etc.
Bias-variance trade off, Overfitting & Underfitting of Models
NLP with Machine Learning Models like Naive-Bayes, Support Vector Machines

Module 4

Text Feature Extraction, Bag of Words (BOW), TF-IDF (Term Frequency - Inverse Document Frequency)
Document Term Matrix (DTM), word2vec, GloVe Vectors
Topic Modelling, Input Embeddings
Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA)
NLP for Text Classification
NLP for Sentiment Analysis

Module 5

Generative AI: Introducing ChatGPT, Large Language Models (LLMs)
Understanding LLMs, General Purpose Models, Pre-training and Fine Tuning
Deep- Learning Basics, Transformer Architecture, Input Embeddings
Multi-headed Attention, Feed-Forward Layer, Masked multi-head Attention
Understanding GPT, Open AI API, Generating Text, Customizing GPT output and Fine Tuning
Hugging Face, Transformer Pipeline, Pre-trained tokenizers, Special tokens
Q&A models: BERT architecture, Tokenizer, Embeddings, Calculating response
Creating QA bot, BERT, RoBERTa, DistilBERT, GPT vs BERT vs XLNET, XLNET Embeddings and Fine Tuning

Prerequisites and eligibility:

No coding experience in any programming language required. We’ll start from scratch.
This course can be taken up by any undergraduate/postgraduate student of Basic & Applied Sciences, Engineering, Management and Computer Applications and also by Research Scholars/Faculties/Working Professionals who want to upskill themselves.
Participants need to have a laptop/PC (with a minimum of 4 GB RAM, 100 GB HDD, Intel i3 processor) and proper internet/Wi-Fi connection.

Contact Person: Dr. Subrat Kotoky

Email: [email protected] / [email protected]

Phone: 9085317465 / 8473874389

Expert Profile: Mr. Shreyas Shukla

Professional Corporate Trainer & Microsoft Azure Certified Data Engineer

M.Tech-IIT Kharagpur & BE- The Aeronautical Society of India, New Delhi

Has successfully conducted 25+ courses and trained 1500+ learners in the fields of Python Programming, Data Analytics, Machine Learning, Deep Learning, Computer Vision etc. till now.

(Total Experience in conducting Professional Courses: 4+ Years)

Certifications: