An intensive bootcamp covering the full machine learning engineering lifecycle. From classical algorithms to production deployment with FastAPI, Docker, and CI/CD -- build ML systems that work in the real world.
Master the foundational ML algorithms. Understand gradient descent, regularisation (L1/L2), feature importance, and when regression is the right tool for the job.
Learn tree-based models from single decision trees to powerful ensemble methods. Feature importance, pruning strategies, and why Random Forests remain a go-to in industry.
Support Vector Machines for classification and regression. Gradient Boosting (XGBoost, LightGBM, CatBoost) -- the algorithms that win Kaggle competitions and power production systems.
The art and science of creating powerful features. Encoding strategies, feature interactions, polynomial features, domain-specific feature design, and automated feature engineering.
Cross-validation, hyperparameter tuning (Grid, Random, Bayesian), learning curves, bias-variance tradeoff, and selecting the right metric for your business problem.
Track experiments systematically with MLflow. Log parameters, metrics, and artefacts. Compare runs, reproduce results, and manage the model lifecycle professionally.
Serve models as APIs with FastAPI. Containerise with Docker. Handle input validation, batch prediction, model versioning, and A/B testing in production.
Automated testing for ML pipelines. Data validation, model quality gates, continuous training, and GitOps workflows that keep your models fresh and reliable.
Build and deploy a complete ML system end-to-end: data ingestion, feature engineering, model training, evaluation, API deployment, and monitoring. Showcase-ready portfolio piece.
Python developers who want to transition into machine learning engineering roles
Data analysts looking to build and deploy predictive models in production
Software engineers who need to integrate ML models into existing systems and pipelines
Python basics and basic math required. You should be comfortable with Python programming (functions, classes, data structures) and have a basic understanding of statistics (mean, standard deviation, probability). Our Python for AI course provides ideal preparation.
Interactive sessions with real-time Q&A and screen sharing
All sessions recorded and available for 12 months after the course
Real-world projects that build your portfolio as you learn
Personal mentoring sessions to address your specific questions
Industry-recognised certificate upon successful completion
Our instructors are seasoned practitioners with years of experience building production AI systems. They hold certifications across major cloud platforms and have trained thousands of professionals worldwide.
Data science courses often focus on analysis and notebooks. This bootcamp focuses on engineering -- building ML systems that run in production with proper testing, deployment, monitoring, and CI/CD. You will ship working ML APIs.
No. Classical ML algorithms run efficiently on standard laptops. We use cloud environments for anything compute-intensive. A laptop with 8GB RAM and a stable internet connection is sufficient.
Yes. The final two weeks are dedicated to an end-to-end capstone project where you build, deploy, and document a complete ML system. This becomes a powerful portfolio piece for job interviews.
Scikit-learn, XGBoost, LightGBM, MLflow, FastAPI, Docker, GitHub Actions, and pytest. These are the industry-standard tools used by ML engineering teams worldwide.