AI is the defining technology of this generation — and India is investing Rs. 10,371 crore in IndiaAI Mission. This track doesn't just teach AI as a subject — it makes students builders of AI systems that solve real Indian problems: crop disease detection, healthcare prediction, regional language processing, and smart city automation. Every project connects to something that matters in India.
Understand how AI and Machine Learning actually work — not just buzzwords
Build complete ML pipelines: data collection → training → testing → deployment
Work with Python, TensorFlow, scikit-learn, OpenCV on real datasets
Design AI systems for Indian agriculture, healthcare, and smart cities
Understand AI ethics — bias, privacy, and responsible AI in Indian context
Publish a research paper or present at the National Symposium
Master the four pillars of modern Artificial Intelligence through hands-on application.
What is AI? From chess to ChatGPT to Ola Surge pricing. Learn the hierarchy: AI vs ML vs Deep Learning. How machines learn from data — supervised vs unsupervised.
Classification & Regression. Learn what is a model, training, testing, validation. Predict crop yield based on rainfall. Use Scikit-Learn — fit, predict, score.
How the human brain inspired AI — neurons and synapses. Artificial Neural Networks — layers, weights, activation functions. Backpropagation explained with water flow analogy.
AI That Sees. OpenCV library — image loading, processing, filters. Convolutional Neural Networks (CNNs) — visual explanation. Object detection concepts — YOLO introduction.
A structured journey from data science basics to full AI research project deployment.
Data Science Basics
Topics Covered:
• What is AI? — From chess to ChatGPT to Ola Surge pricing
• AI vs ML vs Deep Learning — the hierarchy explained
• How machines learn from data — supervised vs unsupervised
• Introduction to Python for data: NumPy, Pandas
• Loading and exploring datasets (Indian datasets from data.gov.in)
• Data visualization with Matplotlib and Seaborn
Classification & Regression
Topics Covered:
• What is a model? Training, testing, validation
• Linear regression — predict crop yield based on rainfall
• Classification — spam detection, disease prediction
• scikit-learn library — fit, predict, score
• Model evaluation: accuracy, precision, recall, F1
• Overfitting and underfitting — with Indian cricket analogy
Deep Learning
Topics Covered:
• How the human brain inspired AI — neurons and synapses
• Artificial Neural Networks — layers, weights, activation functions
• Backpropagation explained with water flow analogy
• Introduction to TensorFlow and Keras
• Building your first neural network
• GPU vs CPU for AI — Google Colab setup
AI That Sees
Topics Covered:
• What is Computer Vision?
• OpenCV library — image loading, processing, filters
• Convolutional Neural Networks (CNNs) — visual explanation
• Object detection concepts — YOLO introduction
• Face recognition — how Aadhaar verification works
• Real-time camera feed processing
Responsible AI
Topics Covered:
• Natural Language Processing — how AI understands Hindi and English
• Text classification, sentiment analysis
• Building a simple chatbot for Indian users
• AI in Indian languages — challenges and solutions
• AI Ethics: bias in AI, privacy (Aadhaar concerns), deepfakes
• Responsible AI — India's NITI Aayog AI principles
AI Research Project
Topics Covered:
• Problem selection — Indian real-world AI opportunity
• Full ML pipeline: data → model → evaluate → deploy
• Model deployment on Streamlit or Flask
• Research paper writing — introduction, methodology, results
• Poster creation and pitch deck
• National Symposium presentation
Work with Python, TensorFlow, scikit-learn, and OpenCV.
Load and explore datasets from data.gov.in (Census, GDP).
Publish a research paper or present at the National Symposium.
Understand AI ethics, bias, privacy, and NITI Aayog AI principles.
Select an Indian real-world AI opportunity and build a complete ML pipeline: data → model → evaluation → deployment.
CNN model that identifies wheat/rice/cotton diseases from leaf photos.
ML model to predict diabetes/TB risk from patient symptoms data.
NLP model to identify misinformation in Hindi WhatsApp forwards.
Predict AQI levels in Delhi NCR using weather + traffic data.
Convert Indian Sign Language gestures to text using OpenCV.
Predict student JEE performance based on practice test patterns.
ML model for optimal irrigation scheduling based on soil + weather.
Real-time threat detection + SMS alert using Raspberry Pi + CV.
These modules run across all three tracks during Week 1 and Week 6 of every cohort. They build the research mindset, communication skills, and innovation culture that make FSRIP students stand out.
What is Research? — How great Indian scientists approached problems
Problem Identification Framework — finding real problems around you
Literature Review basics — how to read and cite papers
Research Questions & Hypothesis formation
Data Collection methods — primary vs secondary, Indian datasets
How to write a Research Paper — structure, abstract, conclusion
Research ethics — plagiarism, data privacy, attribution
Using Google Scholar, ResearchGate, and Indian research portals
Understand real users — interview a farmer, shopkeeper, teacher about their problems
Problem statement using '5 Whys' Indian context case studies
Brainstorming with Indian constraints — low cost, low bandwidth, multilingual
Build AI solutions for real Indian challenges and showcase your innovation at the National Symposium. Publish a research paper and deploy your application.
Artificial Intelligence & Machine Learning