A model finds useful patterns in training data, then applies them to new cases. This simple idea leads to big questions about labels, features, data quality, and whether a pattern will hold outside the training set.

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Search rankings, translation, fraud alerts, delivery routes, medical imaging, and personalized feeds all use machine learning. These systems often support human judgment rather than replace it.

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A model can be accurate in testing yet fail when the world changes or the data leaves people out. Evaluation, uncertainty, privacy, bias, and fairness help determine whether a prediction is truly useful.

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Data scientists, machine learning engineers, researchers, designers, and domain specialists collaborate on systems and open-source tools. Projects range from classifying plants and detecting defects to forecasting energy use and creating responsible AI products.

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Review this chapter with practice based on your mistakes.