Data Preparation for Machine Learning
Databricks
Last updated June 30, 2026
Data Preparation for Machine Learning is a free, two-hour, hands-on course on the work that decides whether a model ever performs: getting the data ready. It covers exploring a dataset to understand what you're working with, cleaning it of the errors and gaps that quietly wreck results, and organizing it into a shape a model can learn from. The exercises use Python on the Databricks platform. It assumes you can write Python and know basic machine learning concepts, and it's a practical foundation for anyone moving toward training their own models.
What you'll learn
- Exploring a dataset to understand its contents and quirks
- Cleaning data: handling errors, gaps, and inconsistencies that derail models
- Organizing and shaping data into a form a model can learn from
- Practical data-preparation workflows in Python
- Doing the work on the Databricks platform
Frequently asked questions about Data Preparation for Machine Learning
Who is Data Preparation for Machine Learning for?
Developers with Python and basic machine learning knowledge who want hands-on practice exploring, cleaning, and organizing data for model training.
Is Data Preparation for Machine Learning free?
Yes — Data Preparation for Machine Learning is completely free to take.
What are the prerequisites for Data Preparation for Machine Learning?
Python; basic machine learning concepts; familiarity with Databricks. A free Databricks Academy account is required to start.
Do you need to code for Data Preparation for Machine Learning?
Yes — Data Preparation for Machine Learning involves hands-on coding.
Why we suggest this course
Targets the stage that quietly determines model quality — exploring, cleaning, and organizing data — for developers who want to do it properly rather than rush to training. One thing to know: the hands-on exercises run on Databricks and need a free Academy account, so the tooling is specific to that platform.