Package Description:

A 6-week hands-on program where you go from data scientist to ML engineer by building one complete, production-grade machine learning system, deployed, monitored, and live on GitHub. You will work on the IEEE-CIS Fraud Detection dataset (590,000 real e-commerce transactions from Vesta Corporation, which protects $18 billion in annual payments). This is not a toy dataset or a Kaggle submission. You are building a system a real fintech team would recognise. Included: A personalised week-by-week learning plan with daily build targets 6 weeks of structured mentorship with Deepa, including async code reviews on GitHub, architecture feedback, and live Q&A sessions timed to your pace Curated self-study resources mapped to each module, hand-picked to match the exact tools and concepts you are building with AI-assisted coding guidance using Cursor throughout, with specific prompts for every build task An interview question bank covering every module, so you are preparing for interviews while you build Support for your questions and doubts throughout the program 6 structured code reviews where Deepa reviews your actual GitHub PRs with inline comments A project plan document you fill in during the EDA, so you always know what you are building and why The system you build covers the full production ML stack: model training and experiment tracking, a live prediction API, containerization and deployment, drift monitoring, explainability, and an automated CI/CD pipeline, all wired together into one demonstrable system.