Supervised Learning Tutorial

Supervised learning is a fundamental concept in machine learning, where an algorithm learns from labeled training data to make predictions or decisions. This tutorial will guide you through the basics of supervised learning, including different algorithms and practical examples.

Key Concepts

Training Data: Labeled data used to teach the model.
Test Data: Unlabeled data used to evaluate the model's performance.
Model: The algorithm used to learn from the data.

Common Supervised Learning Algorithms

Linear Regression: Used for predicting continuous values.
Logistic Regression: Used for binary classification.
Support Vector Machines (SVM): Used for both classification and regression.
Decision Trees: Used for both classification and regression.
Random Forest: An ensemble method that combines multiple decision trees.

Example

Here's a simple example of how supervised learning works:

Data: A dataset containing information about houses, such as size, number of bedrooms, and price.
Model: A linear regression model to predict the price of a house based on its features.
Training: The model is trained on the dataset, learning the relationship between the features and the price.
Prediction: Given a new house with a size of 2000 square feet and 3 bedrooms, the model predicts a price of $500,000.

Supervised Learning Tutorial

Key Concepts

Common Supervised Learning Algorithms

Example

Further Reading