Machine Learning · Chapter 1 of 40
What is Machine Learning?
Machine Learning (ML) is a subset of AI where models LEARN patterns from data instead of being explicitly programmed with rules.
Given examples of inputs and outputs, an ML algorithm finds a function that maps them — and generalises to new, unseen inputs.
Example 1 (python)
# Traditional programming: rules -> output
# ML: data -> rules (a model)
from sklearn.linear_model import LinearRegression
model = LinearRegression()The scikit-learn library provides ready-to-use ML models.
Example 2 (python)
# Everyday ML: spam filter, product recs, face unlockML powers apps you use daily.
Key points
- ML learns patterns from data.
- Different from rule-based programming.
- Needs quality training data.
- Powers recommendations, translation, vision.
💡 Note: ML is not magic — bad data produces bad models. 'Garbage in, garbage out' applies strongly.