Tech Undergraduates & Graduates

AI Practitioner

A builder's path from Python and statistics to deployed deep learning and GenAI applications — culminating in a shipped capstone.

120 hours · ₹24,999.00

Module-by-module curriculum

M1 — Python & Data Foundations 16 hrs

Python for data science: NumPy, Pandas
Data cleaning and handling missing/messy data
Exploratory data analysis (EDA)
Visualization with Matplotlib and Seaborn
Statistics refresher: distributions, correlation, hypothesis basics

M2 — Mathematics for ML 12 hrs

Linear algebra essentials: vectors, matrices, dot products
Probability and statistics for ML
Calculus intuition behind gradient descent
Optimization basics and loss functions

M3 — Classical Machine Learning 18 hrs

Supervised learning: linear & logistic regression, decision trees, random forests, SVM, kNN
Unsupervised learning: k-means clustering, PCA
Model evaluation: cross-validation, precision/recall, ROC-AUC
Building pipelines with scikit-learn

M4 — Deep Learning Foundations 18 hrs

Neural network fundamentals and backpropagation
Activation functions and regularization
Convolutional Neural Networks (CNNs) for vision
Recurrent networks and LSTMs for sequence data
Framework choice: TensorFlow/Keras or PyTorch

M5 — Natural Language Processing & GenAI 16 hrs

Text preprocessing and embeddings
Transformer architecture overview
Using pretrained LLMs via API
Fine-tuning basics vs. prompting
Introduction to retrieval-augmented generation (RAG)

M6 — Model Deployment & MLOps Basics 14 hrs

Model serialization and versioning
Building APIs with Flask or FastAPI
Containerization basics with Docker
Deploying to a cloud free tier (AWS/GCP)
Monitoring and logging fundamentals

M7 — AI Product Thinking & Ethics 10 hrs

Responsible AI principles and frameworks
Bias and fairness testing techniques
Data privacy and security in ML systems
The AI product lifecycle, from idea to production

M8 — Capstone Project 16 hrs

Problem selection and solution architecture
Build, test, deploy and document
Individual or 2–3 person teams, mentor-guided

Capstone project

Full-Stack AI Application

Build a full-stack AI application end-to-end: data pipeline, trained model, deployed API, and a simple demo frontend. Upload your source code (zip or repo link), deployment link/screenshots, and a written report.