Page 44 - AI & Machine Learning for Beginners: A Guided Workbook

systematic adjustments, and rigorous evaluation to create a model
that performs reliably on new data.

In summary

In this example:

• Data Collection: We collected key features (color and
alcohol) to distinguish between beer and wine.
• Data Preparation: Ensured our data was clean and
properly split for training and testing.
• Model Selection & Training: A simple linear model was
chosen, and through iterative training steps, its parameters
were optimized.
• Evaluation & Tuning: The model was evaluated on unseen
data, and hyperparameters were adjusted for improved
accuracy.

By following these steps, even a beginner can begin to understand
the process behind training AI models. This framework applies to
many other domains, making it a fundamental skill in the
development of reliable AI applications.

Here are some questions to test your understanding of

the "Beer vs. Wine" AI training example
Question (Defining the Problem): Why do we begin

by asking “What is the difference between wine and beer?”
and how does this question guide the entire training
process?
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