The Machine Learning Data Process
The Machine Learning Data Process
Short description explaining what the learner will understand after completing this study map.
From Problem to Working Model
A machine learning project begins with a problem, not an algorithm. The problem is analyzed, suitable data is selected and prepared, the data is explored, a model is trained and tested, and the results are reviewed before deployment.
Core idea: the process is iterative. Training, testing, reviewing, and improving may be repeated as the team learns from the results.
Essential Process Terms
Tap each card to review the most important activities in the machine learning data process.
Preparing the Data
Raw data often needs preparation before it can be used effectively. Common activities include cleaning, integration, transformation, reduction, and wrangling.
Training vs Testing
Training data is used to help the model learn. Test data is kept for checking how well the trained model performs on data that was not used for learning.
Training
The selected algorithm learns from prepared training data. This is where the model develops the patterns or relationships it will use.
Testing
Separate test data is used to assess how the trained model performs. Testing provides evidence for review, comparison, and further improvement.
The End-to-End Process
The exact workflow can vary, but a useful sequence moves from understanding the problem to preparing data, selecting and checking algorithms, improving results, presenting findings, and deploying a satisfactory model.
Pre-processing and Review
Data quality strongly affects model quality. Preparation makes data more suitable for analysis, while review checks whether both the data and the chosen algorithms are performing well enough to proceed.
Process Explorer
Explore the major stages of the machine learning data process and what each stage contributes.
Key Takeaways
Focus on the flow: understand the problem, define and prepare the data, check algorithms, improve the results, present findings, and deploy when performance is satisfactory.
Core pattern: analyze the problem → select data → pre-process → visualize → select a model → train → test → repeat and improve → review → deploy.
Common mistake: the process is not necessarily a one-way pipeline. Results from testing and review can send you back to the data, preprocessing choices, or algorithm selection.
Knowledge Check
These questions test whether you can recognize the major stages and purposes within the machine learning data process.