Introduction to Scikit-Learn

Scikit-Learn (also written sklearn) is the most widely used machine learning library in Python. It provides simple, efficient tools for data mining and data analysis, built on NumPy, SciPy, and Matplotlib. Scikit-Learn implements a consistent API across all its algorithms, making it easy to experiment with different models.

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Machine Learning Basics

Machine learning is a subset of artificial intelligence where algorithms learn patterns from data to make predictions or decisions.

Types of Machine Learning

Type	Description	Example
Supervised Learning	Learn from labelled data (input-output pairs)	Predicting house prices, classifying emails as spam
Unsupervised Learning	Find patterns in unlabelled data	Customer segmentation, anomaly detection
Reinforcement Learning	Learn by interacting with an environment	Game playing, robotics (not covered by Scikit-Learn)

Supervised Learning Tasks

Task	Output	Example
Classification	Discrete category	Spam/not spam, dog/cat/bird
Regression	Continuous value	House price, temperature, revenue

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The Scikit-Learn API

Every Scikit-Learn model follows the same pattern:

from sklearn.some_module import SomeModel

# 1. Create the model
model = SomeModel(hyperparameter=value)