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Machine Learning Basics

Machine learning teaches computers to make predictions or decisions from data.

What Is Machine Learning​

Machine learning models learn patterns from examples instead of being programmed with every rule manually.

Common tasks include classification, regression, clustering, recommendation, and anomaly detection.

How To Use Machine Learning​

Collect data, prepare features, train a model, evaluate it, and deploy it where it can make predictions.

Basic Example​

from sklearn.linear_model import LinearRegression

model = LinearRegression()
model.fit([[1], [2], [3]], [2, 4, 6])
print(model.predict([[4]]))

Common Concepts​

  • Training data teaches the model.
  • Features describe inputs.
  • Labels describe expected outputs.
  • Metrics measure model quality.

What To Learn Next​

Learn train/test splits, overfitting, feature engineering, model evaluation, and deployment.