The Foundations of Learning: 6 Types of Learning in Machine Learning

Machine learning is a field of artificial intelligence (AI) that focuses on developing algorithms and models that enable computers to learn and make predictions or decisions without being explicitly programmed for each task. It’s a subset of AI that leverages statistical techniques to empower machines to improve their performance on a specific task through experience.

I. Supervised Learning:

1. Definition:

• Supervised learning is a type of machine learning where the algorithm is trained on a labeled dataset. The algorithm learns from the input-output pairs, making predictions or decisions when new, unseen data is presented.

2. Key Characteristics:

• The training dataset includes both input features and corresponding target labels.
• The goal is to learn a mapping function that can accurately predict the output for new, unseen inputs.

3. Examples:

• Regression: Predicting a continuous output (e.g., house prices).
• Classification: Assigning inputs to predefined categories (e.g., spam or not spam).

II. Unsupervised Learning:

1. Definition:

• Unsupervised learning involves algorithms that are trained on an unlabeled dataset. The system tries to learn the…