Data Science
Master the future of AI. Build intelligent applications using Large Language Models, prompt engineering, and cutting-edge Gen AI tools to create real-world solutions.
Overview
Curriculum
Comprehensive learning path designed by industry experts
Career Outcomes
- Data Scientist
- ML Engineer
- AI Application Developer
- Prompt Engineer
Prerequisites
- Python programming basics
- Basic understanding of Machine Learning concepts
- Familiarity with APIs
Real-world capstone projects
Apply your skills to real-world challenges through industry-aligned capstone projects.
E-Commerce Analytics
Predicting Customer Repeat Purchases & Revenue Drivers using Machine Learning on E-Commerce Data
Gender Guesser
Predicting the gender of a person using deep learning
Propensity to Buy
Predicting customer purchase likelihood using machine learning to optimize marketing strategies and improve conversion rates
AI-Powered Chatbot
Build an intelligent conversational AI chatbot using Large Language Models and NLP to provide automated customer support and engagement
Clause Classification
Develop an AI-powered system to automatically classify and categorize legal clauses in contracts using NLP and deep learning techniques for efficient document analysis
PDF Chatbot
Intelligent PDF document chatbot built using Retrieval-Augmented Generation (RAG) architecture with Python and LangChain. This advanced system enables natural language querying of PDF documents by extracting text, creating vector embeddings, and implementing semantic search capabilities. The chatbot leverages Large Language Models to provide accurate, context-aware answers from document content, making it ideal for document Q&A, research assistance, and knowledge extraction from large PDF repositories.
SQL Query Generator
AI-powered SQL query generator built with Retrieval-Augmented Generation (RAG), Python, and LangChain that converts natural language questions into optimized SQL queries. This intelligent system understands database schemas, table relationships, and business context to generate accurate, efficient SQL statements. The generator leverages RAG architecture to retrieve relevant schema information and uses Large Language Models to translate user intent into syntactically correct SQL queries, making database interactions accessible to non-technical users while ensuring query optimization and security.
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Frequently Asked Questions
Everything you need to know about the course