Supervised Learning, Unsupervised Learning, and Reinforcement Learning

These are the primary types of machine learning techniques, each serving different purposes and using different approaches.

Supervised Learning

Definition: Supervised learning involves training a model on a labeled dataset, where the input data (features) and corresponding output labels (targets) are provided. The goal is to learn a mapping function that predicts outputs for new, unseen inputs.

Key Points:

Input: Labeled data (e.g., {input: x, output: y}).

Output: Predicts labels for new inputs.

Algorithm’s Goal: Minimize error between predicted and actual outputs.

Examples:

Classification: Predicting discrete labels.

E.g., Email spam detection (spam or not spam).

Regression: Predicting continuous values.

E.g., Predicting house prices.

Common Algorithms:

Linear Regression, Logistic Regression, Support Vector Machines (SVM), Decision Trees, Neural Networks.

Unsupervised Learning

Definition: Unsupervised learning works with unlabeled data. The model attempts to identify patterns, structures, or groupings in the input data without any explicit guidance.

Key Points:

Input: Unlabeled data.