Train and evaluate a classification model (e.g. disease detection or churn prediction), then expose it through a Flask API.
A project-based virtual internship where you take ML models from a baseline to a deployed app using scikit-learn, XGBoost, and Flask — on real datasets, reviewed by a mentor, with a verifiable certificate at the end.
Train and evaluate a classification model (e.g. disease detection or churn prediction), then expose it through a Flask API.
Build a regression model with feature engineering and proper validation, tuned with XGBoost for real accuracy gains.
Wrap your trained model in an interactive Streamlit app so anyone can use it — a standout portfolio piece.
Python · scikit-learn · XGBoost · TensorFlow (basics) · Flask · Streamlit
Students with basic Python who want to move beyond tutorials into real, deployed machine learning — and have the projects to prove it in interviews.
Start free or upgrade for weekly mentor reviews. You finish with deployed ML projects and a verifiable certificate.
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