Advanced Machine Learning with Python

Move from machine learning models to production solutions

Building a model is only half of data science in industry; the other half is delivering models that are fast to produce, handle real-world data patterns, and run reliably in production. On this course, you’ll learn how to take machine learning projects from development through to deployment using advanced techniques in Python and MLOps.

You’ll explore how to accelerate model development with automated machine learning and no-code platforms, while learning how to work with real-world challenges such as limited data availability, AI fairness, and changing data patterns.

By the end of the course, you’ll understand how professional data teams build, deploy, and maintain machine learning solutions.

Accelerate machine learning with AutoML and advanced modelling techniques

Discover how automated machine learning (AutoML) can help streamline the model development process.

You’ll learn how to rapidly build and compare models, generate synthetic data when real datasets are limited, and evaluate the impact of AI fairness considerations in applied machine learning scenarios.

You’ll also explore techniques to help you develop more robust predictive solutions for real-world applications.

Apply MLOps to deploy and manage machine learning models

Learn how MLOps bridges the gap between developing a machine learning model and delivering it as a reliable production service. You’ll explore CI/CD pipelines, containerisation, and service-based deployment to make models easier to manage and scale.

Through a project, you’ll apply your skills by defining a business problem, engineering features, training and tuning a model, deploying it as a service, and monitoring it in production.

By the end, you’ll be ready for machine learning engineer and applied data scientist roles.

This course is designed for learners who can already build and evaluate machine learning models in Python.

It is ideal for aspiring machine learning engineers, data scientists who want to operationalise their work, and developers preparing for roles where models must be deployed and maintained, not just trained.

Learners should be confident with the end-to-end modelling workflow, including feature engineering, model selection, and evaluation, before starting.

This course is designed for learners who can already build and evaluate machine learning models in Python.

It is ideal for aspiring machine learning engineers, data scientists who want to operationalise their work, and developers preparing for roles where models must be deployed and maintained, not just trained.

Learners should be confident with the end-to-end modelling workflow, including feature engineering, model selection, and evaluation, before starting.

• Explain AutoML concepts, compare AutoML frameworks, and apply H2O to automate machine learning workflows.
• Apply MLflow and DVC to track experiments, version datasets, and reproduce machine learning workflows.
• Evaluate AutoML search strategies, model performance, and hyperparameter configurations to support reliable model selection.
• Demonstrate model serving and deployment workflows using FastAPI, Docker, and cloud-based approaches.
• Identify model performance changes and data drift that indicate the need for model retraining.
• Describe how monitoring, observability, and automated retraining support reliable machine learning systems in production.