AI & Machine Learning Internship
AI & ML
Build intelligent apps — Python, ML, Data Science, Deep Learning
Learn Python, data science, machine learning, deep learning, and AI project deployment. Work on real datasets and build a portfolio with mentor support.
Duration 3 months
Eligibility Students & Graduates
Mode Remote + Onsite (Bangalore)
Small batch, weekly mentor reviews, code audits, and QA cycles.
Certificate: Hallunix verified certificate on completion.
What you'll learn
End-to-end AI/ML workflow — from data to model deployment.
- Python for Data Science
- Data Cleaning & Visualization
- Machine Learning Algorithms
- Deep Learning (Neural Networks)
- Model Evaluation & Tuning
- Deployment on Cloud (AWS/GCP)
- Version Control: Git & GitHub
Responsibilities
- Complete weekly ML milestones and submit code for review
- Document experiments and results
- Participate in code reviews and QA cycles
- Present final project with deployment and documentation
Benefits
- 1:1 mentorship and code reviews
- Certificate & letter of recommendation
- Portfolio-ready ML project
- Interview prep & placement assistance for top performers
Timeline & Capstone
Onboarding
Week 0 — Setup & environment
Core Modules
Weeks 1–8 — ML sprints
Capstone
Weeks 9–12 — Final project & presentation
Sample Certificate
Official Hallunix Internship Certificate
PYQs & Official FAQ
Q: Which ML library should I use?
A: scikit-learn for classical ML, TensorFlow/Keras for deep learning. Both are covered in the program.
Q: Will I deploy my model?
A: Yes — the capstone includes cloud deployment steps. You will learn AWS/GCP basics and CI/CD pipelines.
Q: What is the certificate format?
A:
HTS/JUN25/INT-XX — Official Hallunix Internship Certificate with unique ID.Q: What are the official PYQs?
- Explain supervised vs unsupervised learning with examples.
- How does a neural network learn? Illustrate with diagrams.
- Describe overfitting and how to avoid it in ML projects.
- How do you evaluate ML model performance? List key metrics.
- How to deploy a model using Flask or FastAPI? Outline the steps.
- What is the role of Git & GitHub in ML workflows?
- Explain the difference between accuracy and precision.
- How do you tune hyperparameters in deep learning?
- What is cross-validation and why is it important?
- List steps for cleaning and visualizing data in Python.
Ready to build AI?
Apply now — limited seats for hands-on mentorship.
Need help? Contact: hallunix.tech@gmail.com