Page 45 - AI & Machine Learning for Beginners: A Guided Workbook

Questions (Data Collection & Features): What two

features are used in this example to distinguish between
beer and wine?
Follow-up: Why might these particular features be useful for
classification?
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Question (Data Preparation): Explain the importance

of shuffling and splitting the data into training and test sets.
How does this help improve the model’s accuracy?
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Question (Model Training): In the linear model

represented by the equation y = m*x + b, what do the
parameters m and b represent in the context of the training
process?
Follow-up: How does the iterative adjustment of these
parameters improve the model’s predictions?
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