E.C Classic ML Roadmap

Use this elective when your dataset is small, your features are clear, or you need a strong baseline before trying a heavier model.

See the Baseline Map First

Classic ML helps you answer: is the problem already solvable with simple features?

Run the Smallest KNN Baseline

def distance(a, b):
    return sum((x - y) ** 2 for x, y in zip(a, b)) ** 0.5

train = [
    ([0.1, 0.2], "low"),
    ([0.2, 0.1], "low"),
    ([0.8, 0.9], "high"),
    ([0.9, 0.8], "high"),
]

point = [0.75, 0.85]
nearest = min(train, key=lambda row: distance(row[0], point))
print("prediction:", nearest[1])
print("neighbor:", nearest[0])

Expected output:

prediction: high
neighbor: [0.8, 0.9]

This is the smallest baseline habit: define features, compare distance, predict, and keep the result for later comparison.

Learn in This Order

Step	Lesson	Practice Output
1	E.C.1 SVM	Explain margin, support vectors, C, and kernel choice
2	E.C.2 KNN	Build a distance-voting baseline
3	E.C.3 Naive Bayes	Convert evidence counts into class probabilities
4	E.C.4 LDA	Project features to separate classes

Pass Check

You pass this module when you can build one classic baseline, explain why it is appropriate, and compare it with a heavier model or later project result.