ML Training with Internship
Become a production-ready ML engineer. Master classical and modern ML, MLOps fundamentals and deployment, then complete an internship building, deploying and monitoring real ML models at a partner company.
Become a production-ready ML engineer. Master classical and modern ML, MLOps fundamentals and deployment, then complete an internship building, deploying and monitoring real ML models at a partner company.
Syllabus
- ML Algorithms
- Feature Engineering
- Model Selection & Tuning
- Model Deployment
- MLOps Basics
- A/B Testing
- Internship Project
- Placement Prep
What you will learn
- Deploy production ML models
- Run A/B tests
- Monitor and retrain models
- Demonstrate real ML experience
Tools you will use
- Scikit-learn
- Python
- Jupyter
- MLflow
- FastAPI
- Docker
Projects
- Production ML model at partner
- A/B test analysis report
- MLOps pipeline build
- Recommendation letter
Career paths
- ML Engineer
- Data Scientist
- AI Analyst
- Applied ML Engineer
Who should join
- Data enthusiasts
- Python developers
- Analysts going technical
Prerequisites
- Graduate or final-year student
- Commitment to attend full program
- Basic computer skills
Certificate
Course Completion Certificate + Internship Experience Letter from partner companies.
Syllabus
- ML Algorithms
- Feature Engineering
- Model Selection & Tuning
- Model Deployment
- MLOps Basics
- A/B Testing
- Internship Project
- Placement Prep
Learning outcomes
- Deploy production ML models
- Run A/B tests
- Monitor and retrain models
- Demonstrate real ML experience
Tools
- Scikit-learn
- Python
- Jupyter
- MLflow
- FastAPI
- Docker
Projects
- Production ML model at partner
- A/B test analysis report
- MLOps pipeline build
- Recommendation letter
Career paths
- ML Engineer
- Data Scientist
- AI Analyst
- Applied ML Engineer
Who should join
- Data enthusiasts
- Python developers
- Analysts going technical
Prerequisites
- Graduate or final-year student
- Commitment to attend full program
- Basic computer skills
Frequently asked questions
Is prior ML knowledge needed?
Basic Python is enough — we cover ML from fundamentals through production.
Which tools and technologies will I actually use?
You work hands-on with Scikit-learn, Python, Jupyter, MLflow, FastAPI and Docker throughout the course, rather than only studying them in theory.
What will I be able to do by the end?
By the end you can: Deploy production ML models; Run A/B tests; Monitor and retrain models; Demonstrate real ML experience.