[Concept Entry] AI: Supervised Learning

content/ai/concepts/supervised-learning/supervised-learning.md

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+---
+Title: 'Supervised Learning'
+Description: 'Supervised learning is a machine learning technique where algorithms learn from labeled data to make predictions.'
+Subjects:
+  - 'Machine Learning'
+  - 'Data Science'
+  - 'Artificial Intelligence'
+Tags:
+  - 'AI'
+  - 'Deep Learning'
+  - 'Classification'
+  - 'Regression'
+  - 'Predictive Modeling'
+CatalogContent:
+  - 'machine-learning'
+  - 'paths/data-science'
+---
+
+**Supervised learning (ML)** is a type of machine learning where the algorithm learns from labeled data. It involves training a model on input-output pairs to generalize and predict outcomes for new, unseen data. This label acts as a "supervisor," guiding the learning process.
+
+Imagine teaching a child by showing them examples with correct answers. Similarly, the algorithm learns patterns from these examples and uses them to make predictions on new, unseen data.
+
+**Examples:** Identifying Handwritten Digits, Predicting the prices of cars, Spam emails detection.
+
+**Key Components:**
+
+- **_Training Data:_** A dataset of input-output pairs (e.g., emails labeled as "spam" or "not spam").
+- **_Model:_** The algorithm (e.g., decision tree, neural network) that learns from the data.
+- **_Loss Function:_** Measures how far the model's predictions are from the true labels (e.g., mean squared error for regression, cross-entropy for classification).
+- **_Optimization:_** Adjusting the model’s parameters (weights) to minimize the loss (e.g., using gradient descent).
+
+## Types of Supervised Learning
+
+### Classification
+
+In Classification, the algorithm learns from labeled training data, where each input is associated with a specific class, and then uses this knowledge to classify new, unseen data.
+
+**Examples:** Spam Detection, Handwritten Digit Recognition, Image Classification, Medical Diagnosis.
+
+**Types of Classification:**
+
+- **_Binary Classification:_** The task of classifying data points into one of two classes.
+- **_Multi-class Classification:_** The task of classifying data points into one of more than two classes.
+- **_Multi-label Classification:_** The task of assigning multiple labels to each data point. This is different from multi-class classification, where each data point can only belong to one class.
+
+**Common Classification Algorithms:** Logistic Regression, Support Vector Machines (SVMs), Decision Trees, Random Forests, Naive Bayes, K-Nearest Neighbors (KNN)
+
+### Regression
+
+Regression is a supervised learning task focused on predicting a continuous numerical output. Unlike classification, which assigns data points to categories, regression aims to estimate a value within a range.
+
+**Examples:** House Price Prediction, Stock Price Prediction, Temperature Forecasting, Sales Forecasting.
+
+**Types of Regression:**
+
+- **_Linear Regression:_** Models a linear relationship between inputs and a target variable by finding the line of best fit that minimizes the sum of squared errors.
+- **_Polynomial Regression:_** Used when the relationship between the features and the target variable is non-linear. It fits a polynomial curve to the data.
+- **_Multiple Linear Regression:_** Used when there are multiple input features influencing the target variable.
+- **_Support Vector Regression (SVR):_** Uses SVM principles to find the best-fitting hyperplane within a margin of error.
+- **_Decision Tree Regression:_** Uses a tree structure where nodes represent feature-based decisions, and leaves represent predicted values.
+- **_Random Forest Regression:_** An ensemble method that combines multiple decision trees to improve prediction accuracy and reduce overfitting.
+- **_Neural Network Regression:_** Uses neural networks to learn complex non-linear relationships between features and the target variable.
+
+**Common Classification Algorithms:** Linear Regression, Polynomial Regression, Support Vector Regression (SVR), Decision Tree Regression, Random Forest Regression, Neural Network Regression.