Tech Fixx · Video
Machine Learning Workflow: From Raw Data to Predictions
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From the video description
A machine-learning project involves more than pressing “train”. This beginner overview follows the complete workflow from defining a useful problem to collecting data, preparing features, training a model and checking predictions in the real world.
The workflow covers: • Define the goal and success measure • Collect and understand relevant data • Clean data and create features • Split training, validation and test sets • Train and compare models • Evaluate errors and fairness • Deploy, monitor and update the system
Models can degrade when real-world data changes, so monitoring and review remain important after deployment.
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