AI & Data Science from
Vishlesan i-Hub Foundation
IIT Patna

Build a strong base in Python for data analysis and AI work.

Topics Covered

Python ecosystem overview

Jupyter Notebook & VS Code setup

Installing Python & packages

Writing and running scripts

Variables & data types

Operators & expressions

Conditional statements

Loops & iteration

Input/output formatting

Lists, tuples, sets, dictionaries

Nested & indexed data structures

Functions & lambda functions

Clean reusable code

Exception handling

Mini project & assessment

Go deeper into Python by applying advanced concepts for data transformation, analytics, and visualization workflows.

Topics Covered

Functional programming concepts

map(), filter(), reduce() for preprocessing

Memory-efficient data reading

Data handling

OOP for Data Science libraries

NumPy arrays & operations

Indexing, slicing & broadcasting

Pandas analysis & data cleaning

GroupBy, joins & transformations

Matplotlib & Seaborn visualization

Learn how data is stored, organised, and queried using relational databases. This module focuses on writing effective SQL queries for analysis, reporting, and decision-making.

Topics Covered

Understanding Relational Databases

Writing And Executing SQL Queries

Filtering And Sorting

Using Joins And Subqueries

Performing Data Aggregations

Applying Window Functions

Indexing Basics

Learn core machine learning algorithms and model-building workflows across supervised, unsupervised, and time series use cases.

Topics Covered

Regression and Classification Foundations

Linear & Logistic Regression

Decision Trees and Random Forests

Gradient Boosting & XGBoost Basics

Clustering Techniques (K-Means, Hierarchical)

Dimensionality Reduction with PCA

Model Evaluation Metrics & Validation

Hyperparameter Tuning Methods

Time Series Analysis & Use Cases

ARIMA and SARIMA Forecasting

Solve real-world business problems using Applied Machine Learning. This module focuses on interpreting results, understanding business impact, and making data-driven decisions.

Topics Covered

Building Churn Prediction Models

Designing Recommendation Systems (Netflix-style)

Customer Segmentation Projects

COVID-19 Forecasting Case Study

Cardiac Heart Disease Prediction

Business Interpretation & Reporting

Understand and build neural networks for complex AI tasks. Learn how deep learning models work behind modern AI systems.

Topics Covered

Understanding Neural Networks

Working With Activation Functions

Selecting Appropriate Loss Functions

Applying Backpropagation Techniques

Using Optimisation Algorithms

Learning CNN Basics

Working With Sequence Models

Work with text and image data using modern AI techniques. This module introduces how machines understand language and visuals, with practical examples from real applications.

Topics Covered

Text Preprocessing

Word Embeddings

Text Classification

Image Preprocessing

Training CNN-based Classifiers

Understanding Transformer Fundamentals

Get introduced to Generative AI, LLMs, and prompt-based workflows. Learn how these models work, how they are used, and how to interact with them safely and effectively.

Topics Covered

Understanding LLM Fundamentals

Working with Embeddings

Exploring Hugging Face Pretrained models

Prompt Engineering

Responsible AI Practices

Deep dive into LLM architectures, fine-tuning, and Prompt Engineering techniques. This module focuses on deeper concepts such as model optimisation, reasoning, and prompt engineering workflows.

Topics Covered

Understanding Transformer Architecture

Exploring types and use cases of Transformers

Fine-Tuning Large Language Models

Prompt Engineering

Build AI systems that combine LLMs with external knowledge sources. This module shows how AI can search, retrieve, and respond using real knowledge sources.

Topics Covered

Creating Vector Embeddings

Implementing Semantic Search

Exploring Vector Databases

RAG Architecture

Developing AI Chat Systems

Build practical AI agents that use tools, reasoning steps, and workflows to automate data science and ML tasks.

Topics Covered

Agentic AI basics & use cases

Agents vs prompts vs workflows

Tool usage & reasoning loops

ReAct & chain-of-thought patterns

Tool calling with Python

Multi-step agent workflows

Agents for data & ML tasks

ML workflow agent project

Learn basic concepts of deploying ML models, using simple cloud resources, container workflows, and model tracking approaches.

Topics Covered

Cloud usage concepts for ML

Handling model and data artifacts

Container workflow basics

Model serving fundamentals

Experiment tracking concepts

Model versioning basics

Prepare resumes, portfolios, and interview skills for AI and Data Science careers.

Topics Covered

Creating Professional Resumes

Building Strong Portfolios

Optimising GitHub Profiles

Practicing Mock Interviews

Planning a Career Roadmap

Work on a complete AI project from start to finish. You will design, build, and present a real solution that demonstrates your learning and practical skills.

Topics Covered

Project Planning

Designing System Architecture

Developing Models

Evaluating & Refining Results

Presenting The Final Project