High Demand 2 · 3 · 6 months

Machine Learning Internship — Train, Evaluate & Deploy Models

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.

Apply for the ML Internship See the projects
What You'll Build

Models you'll train and ship

Classification
Image or Tabular Classifier

Train and evaluate a classification model (e.g. disease detection or churn prediction), then expose it through a Flask API.

Regression
Forecasting / Price Prediction

Build a regression model with feature engineering and proper validation, tuned with XGBoost for real accuracy gains.

Deployment
Deployed Streamlit ML App

Wrap your trained model in an interactive Streamlit app so anyone can use it — a standout portfolio piece.

What you'll learn

  • Supervised learning: classification and regression with scikit-learn
  • Feature engineering, scaling, and handling imbalanced data
  • Model evaluation — cross-validation, precision/recall, the right metric
  • Gradient boosting with XGBoost
  • Deploying models with Flask or Streamlit
  • Versioning your work and projects on GitHub

Tools & stack

Python · scikit-learn · XGBoost · TensorFlow (basics) · Flask · Streamlit

Who it's for

Students with basic Python who want to move beyond tutorials into real, deployed machine learning — and have the projects to prove it in interviews.

Build models you can actually demo

Start free or upgrade for weekly mentor reviews. You finish with deployed ML projects and a verifiable certificate.

Apply Now
FAQ

Machine learning internship questions

What background do I need?
Basic Python and a willingness to learn the maths as you go. We recommend the Data Science track first if you're brand new, but motivated beginners can start here with the onboarding resources.
Do I actually deploy the models?
Yes. Projects go beyond notebooks — you wrap a trained model in a Flask or Streamlit app so it runs as a real, shareable deployment on your GitHub.
Is the certificate verifiable?
Yes. You receive a QR-verified PDF certificate linked to your GitHub project repositories, so employers can authenticate it instantly.
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