Real, end-to-end AI projects
Industry-grade problem statements, real datasets, and production deployment — across vision, audio, and language.
Crop Disease Detection from Leaf Images
Transfer learning on a real 54,000-image agricultural dataset, deployed as a lightweight, quantized API a farmer's phone can actually call.
Environmental Sound Classification for Smart Monitoring
Real urban audio, converted to spectrograms and classified with the exact CNN techniques from Module 16-19 — proving vision and audio share the same underlying tool.
Fine-Tune and Serve a Domain-Specific AI Course Assistant
Start from a real, pretrained GPT-2, fine-tune it on real AI/ML Q&A data using the exact SFT recipe from Module 32, then serve it with genuine decoding control and a production streaming endpoint.
Loan Default Prediction with Explainable Credit Risk Scoring
Real multi-table loan application data, benchmarked across logistic regression, Random Forest, and LightGBM — with SHAP explainability and a cost-based decision threshold, exactly as a regulated lender actually needs.
Retail Demand Forecasting with Time-Aware Feature Engineering
Real daily sales across 1,115 stores, forecasted with XGBoost and engineered temporal features — proving gradient boosting with good features beats a naive sequence model here.
Credit Card Fraud Detection at Extreme Class Imbalance
Real anonymized transaction data with only 0.17% fraud, benchmarking Random Forest, XGBoost, and Isolation Forest — because at this imbalance level, which approach wins is a genuinely open question worth measuring.