Machine Learning Engineering Courses
11 courses for developers who want to build machine learning systems — turning data into models that train, evaluate, and run in production. Most are hands-on and code-first, covering the workflow from preparing data to deploying a model.
Understanding what machine learning is and building something that works are different skills, and these courses are about the second one. They cover the engineering: how you frame a problem as a learning task, prepare and split data, choose and train a model, measure how well it performs, and get it running reliably outside a notebook. Most are practical — you write code, work with real datasets, and deal with problems such as data leakage, class imbalance and drift that affect how a model performs in production. If you can program and want to make machine learning part of what you build, this is where to start.
Machine Learning Engineering courses
11 courses.
Advanced Machine Learning Operations
Databricks
Amazon SageMaker AI Getting Started
AWS Skill Builder
Create machine learning models
Microsoft Learn
Data Preparation for Machine Learning
Databricks
Elements of AI: Building AI
University of Helsinki & MinnaLearn
Introduction to Amazon SageMaker Notebooks
AWS Skill Builder
Machine Learning at Scale
Databricks
Machine Learning Model Deployment
Databricks
Machine Learning Model Development
Databricks
Operationalize machine learning models (MLOps)
Microsoft Learn
Professional Machine Learning Engineer Certification
Google Skills
Frequently asked questions
- Do these machine learning courses require programming?
- Most do. They're built for people who can already code and want to build and deploy machine learning models, so most involve hands-on work in languages and tools like Python. Each course's page says whether it requires programming.
- How are these different from the Learn AI machine learning courses?
- The Learn AI courses explain how machine learning works conceptually with no coding, while these Develop AI courses are for building real systems — training, evaluating, and deploying models in code.
- How much machine learning should I know before starting?
- It varies by course — some start from first principles for developers new to ML, while others assume you already know the basics and focus on building and shipping, so check the level noted on each course's page.
Key concepts
The foundational terms these courses build on — each chip links to a plain-English definition in the AI Pinnacle glossary.