What is Machine Learning?

Introduction

What is Machine Learning? Machine learning is a branch of Artificial Intelligence that builds systems capable of learning from data. Instead of writing explicit rules for every scenario, you provide examples and let the algorithm discover patterns.

Understanding the topic

How ML differs from traditional programming:

• Traditional: rules + data → output.
• ML: data + desired output → learned rules (model).
• The model generalizes to new, unseen examples after training.
• Quality depends on data volume, quality, and representative features.

Informative example

Minimal scikit-learn workflow:

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

model = RandomForestClassifier()
model.fit(X_train, y_train)
print(accuracy_score(y_test, model.predict(X_test)))

Execution workflow

Best practices

• Start with a simple baseline (logistic regression or random forest) before complex models.
• Document your data sources and preprocessing steps for reproducibility.

Summary

Machine learning learns patterns from data to make predictions or discoveries on new inputs.