MACHINE LEARNING INTERNSHIP PROGRAM

6-Week Cohort · Hands-on, Project-First
  • Covers Python, ML algorithms, model deployment, and real business use-cases.
  • 6-week intensive program — live mentorship + hands-on project building daily.
  • Unlimited access to compute, datasets, and mentor 1:1s throughout the cohort.
Mentored by
◎ Industry ML Engineers
& Data Scientists
Applications open Cohort starting Soon Limited seats · 25 Interns only

Prepare for Success with Career Support

Gain the skills and confidence to build real ML projects, with personalized mentorship including 1:1 guidance, resume support, and interview preparation.

1:1 Mentor Interactions

Resume and LinkedIn Profile Review

Interview Preparation and Mock Demos

ML Project Portfolio Assessment

Why Join Us?

Perks & Opportunities

🎓

Hands-on ML Projects

Real datasets, real deployment

📜

Official Internship

Certificate & Letter of Recommendation

💵

Stipend Based Opportunities

Performance-based opportunities will be provided

🧑‍🏫

1:1 Mentorship

Direct guidance from ML engineers

🕒

Structured & Flexible

6-week guided cohort schedule

Tools You Will Learn

Master the core ML stack used by industry teams — from data handling to model building, training, and deployment.

🎯 ML Internship Program

Learn. Build.
Get Hired.

Join our 6-week hands-on ML internship. Build real deployed projects, get 1:1 mentorship, earn a stipend, and unlock a Pre-Placement Offer.

Apply Now Enquire Now
Student working on ML project

Explore Our Course Modules

A structured 6-week ML curriculum — from Python foundations to a fully deployed model, built through hands-on projects at every stage.

  • › Environment Setup — Python, Jupyter/Colab, Git
  • › Live Build: Your First End-to-End Model
  • › Train/Test Split & Core ML Concepts
  • › NumPy & Pandas Fluency on Real Data
  • › Project: Mini EDA + Baseline Model Challenge
  • › Linear & Logistic Regression from Intuition to Code
  • › Decision Trees & Decision Boundaries
  • › Feature Engineering by Fixing a Bad Model
  • › Evaluation Metrics: Accuracy, Precision, Recall, F1
  • › Project: Solo Classification Challenge
  • › Random Forests, XGBoost & LightGBM
  • › Hyperparameter Tuning Challenge
  • › Handling Missing Data, Imbalance & Encoding
  • › Project Kickoff: Your Assigned Real-World ML Project
  • › Formal Mid-Point Evaluation & 1:1 Feedback
  • › Model Interpretability with SHAP
  • › Cross-Validation & Avoiding Overfitting
  • › Project: Refine Your Model Using Feedback
  • › Deploying Your Model with FastAPI / Streamlit
  • › MLOps Basics: Versioning & Production Awareness
  • › How Companies Actually Use ML (Business Context)
  • › Project: Wrap Your Model into a Demo-Ready App
  • › Final Testing & Documentation
  • › Demo Rehearsal & Presentation Skills
  • › Project: Live Demo Day to Stakeholders
  • › Final Evaluation, PPO Decision & Certification

ML INTERNSHIP PROGRAM REVIEWS

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Aditya

★★★★★

I joined this ML internship right after my final year with only basic Python. The week-by-week structure made everything click — by week 3 I was building real models, not just following tutorials. My mentor's feedback each week pushed me to actually understand what I was doing.

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Darshan BR

★★★★★

Best decision for my ML career start. I learned Python, model building, evaluation, and deployment, and worked on a real capstone project that gave me actual hands-on experience. The mentors were always supportive and patient with every doubt.

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Rahul

★★★★★

Started this journey with zero ML background — just basic Python. The program helped me learn everything from scratch through hands-on projects and weekly mentorship. By Demo Day I had a fully deployed model I was genuinely proud to present.

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Nikil

★★★★☆

Enrolling in this internship was one of the best decisions for my career. Coming in with no technical experience was intimidating at first, but the structured weekly approach made the transition smooth. I'd highly recommend it to anyone starting out.

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Meghana S

★★★★★

The project-first approach made all the difference. Instead of sitting through theory for weeks, I was training models from day two. That momentum kept me motivated through the whole 6 weeks, and I ended up with a project I could actually demo in interviews.