Page 42 - AI & Machine Learning for Beginners: A Guided Workbook

• x: Input (our features — color and alcohol).
• m: Weights (the influence of each feature).
• b: Bias (adds flexibility to the line’s placement).

All weights can be organized into a weights matrix (W),
while biases are grouped into a b_vector (b).

5. Training the Model

• Initial Setup: Start with random values for the weights (W)
and biases (b).
• Prediction: Use the equation to predict whether a given
beverage is beer or wine. Initially, performance will be
poor.
• Iterative Learning:

• Error Comparison: Compare predictions against
the actual labels.
• Adjust Parameters: Fine-tune the weights and
biases using optimization techniques (e.g., gradient
descent) to minimize error.
• Training Steps: Each complete round of
adjustments is a training step, gradually refining the
model until it reliably classifies beverages.

Analogy: Training this model is similar to learning to drive. At first,
mistakes are common, but with practice and error correction, the
performance improves.

Simple Visual Diagram of the Model

[Input Features]
/ \
[Color] [Alcohol Content]
\ /
[Linear Model: y = W*x + b]
|
[Prediction]
(Beer or Wine)
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