Tech Fixx · Video
How AI Models Learn From Data: Training, Loss & Evaluation
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How does a machine-learning model improve during training? This explainer follows the process from input data and an initial prediction to error measurement, parameter updates and evaluation on unseen examples.
You’ll learn: • Preparing features and labels • Making a prediction with a model • Measuring error with a loss function • Updating model parameters through optimisation • Repeating the training cycle • Testing generalisation on validation and test data • Recognising overfitting and underfitting
Training finds patterns that reduce a chosen error measure; it does not give a model human understanding or guarantee correct outputs.
#MachineLearningTraining #AIExplained #DataScience