Alex started by collecting . Every day, Alex baked dozens of batches, slightly changing the ingredients: Batch A: Extra sugar, low heat. Batch B: Less flour, high heat. Batch C: Blueberries added, medium heat.

After weeks of baking, Alex had a mountain of notes. This was the . Alex noticed something interesting:

Alex wanted to bake a muffin that every customer would love. Usually, a chef writes a "classical" recipe: 2 cups of flour, 1 cup of sugar, bake at 350°F. This is like , where a human gives a computer exact, step-by-step instructions to follow.

For every muffin sold, Alex asked customers for a rating. This rating was the "label"—the answer key that told Alex if the muffin was a hit or a miss. Step 2: Finding the Patterns (Training)

Introduction To Machine Learning -

Alex started by collecting . Every day, Alex baked dozens of batches, slightly changing the ingredients: Batch A: Extra sugar, low heat. Batch B: Less flour, high heat. Batch C: Blueberries added, medium heat.

After weeks of baking, Alex had a mountain of notes. This was the . Alex noticed something interesting:

Alex wanted to bake a muffin that every customer would love. Usually, a chef writes a "classical" recipe: 2 cups of flour, 1 cup of sugar, bake at 350°F. This is like , where a human gives a computer exact, step-by-step instructions to follow.

For every muffin sold, Alex asked customers for a rating. This rating was the "label"—the answer key that told Alex if the muffin was a hit or a miss. Step 2: Finding the Patterns (Training)