Databricks Machine Learning Associate

Covers model training basics, evaluation methods, feature preparation, notebook workflows, pipelines and ML processes

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Frame ML problems, targets, and baselines so training starts with clear goals and measurable success.
Prepare features with clean transforms, data leakage checks, and consistent logic you can rerun at scale.
Split data correctly and explain why models overfit, underfit, or behave unstably across runs.
Choose metrics that match the task and interpret results beyond a single score or chart.
Compare models fairly using repeatable evaluation and controlled experiments in Databricks notebooks.
Build notebook workflows that are readable, parameterized, and collaboration-ready for teams.
Turn training steps into simple pipelines with clear inputs, outputs, and repeatable execution.
Spot data quality and feature issues that cause silent model failure in production-like settings.
Translate evaluation findings into next actions: feature changes, retraining, or model rollback.

Basic Python or notebook experience: run cells, read variables, and follow simple functions.
Comfort with tabular data: columns, joins, filters, and basic transforms (SQL knowledge helps).
Familiarity with ML basics: train vs test, features vs labels, and what a prediction means.
A laptop/PC and willingness to reason step-by-step about results and trade-offs.
Optional: prior Databricks exposure is helpful but not required.

Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!

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