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authoredFeb 4, 2025··
[Concept Entry] AI: Supervised Learning (#6029)
* Add sueprvised-learning.md file * Update description * Add types and examples * Check formatting * Fix formatting * Improve readability * Format fix2 * Format fix3 * Fix typo * Remove duplicates * Remove whitespaces * Simplify the line * Modify as per comments * Improvise headings * Update supervised-learning.md minor fixes ---------
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---
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Title: 'Supervised Learning'
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Description: 'Supervised learning is a machine learning technique where algorithms learn from labeled data to make predictions.'
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Subjects:
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- 'AI'
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- 'Data Science'
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- 'Machine Learning'
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Tags:
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- 'AI'
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- 'Deep Learning'
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- 'Classification'
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- 'Regression'
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CatalogContent:
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- 'learn-python-3'
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- 'paths/computer-science'
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---
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**Supervised learning (ML)** is a type of machine learning where an algorithm learns from labeled data. It involves training a model using input-output pairs so it can generalize and make accurate predictions for new, unseen data. The labeled outputs act as a guide, helping the model learn the correct relationships.
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**Examples:** Identifying handwritten digits, predicting car prices based on features, detecting spam emails based on content and metadata.
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### Key Components
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- **Training Data:** A dataset containing input-output pairs (e.g., images labeled with digits or emails marked as spam/not spam).
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- **Model:** A machine learning algorithm (e.g., decision trees, neural networks) that learns patterns from the data.
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- **Loss Function:** A metric that measures how well the model’s predictions match the actual labels. (e.g., Mean Squared Error for regression, Cross-Entropy Loss for classification).
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- **Optimization:** A process of adjusting model parameters to minimize the loss and improve accuracy, often using gradient descent or other optimization techniques.
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## Types of Supervised Learning
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### Classification
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Classification involves training an algorithm on labeled data, where each input is associated with a specific category. The model then classifies new, unseen data based on learned patterns.
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**Examples:** Spam Detection, handwritten digit recognition, image classification, medical diagnosis.
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#### Types of Classification
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- **Binary Classification:** The task of classifying data points into one of two classes.
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- **Multi-class Classification:** The task of classifying data points into one of more than two classes.
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- **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.
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**Common Classification Algorithms:** Logistic Regression, Support Vector Machines (SVMs), Decision Trees, Random Forests, Naive Bayes, K-Nearest Neighbors (KNN)
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### Regression
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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.
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**Examples:** House price prediction, stock price prediction, temperature forecasting, sales forecasting.
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#### Types of Regression
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- **[Linear Regression:](https://www.codecademy.com/learn/linear-regression-mssp):** Models a linear relationship between inputs and a target variable by finding the line of best fit that minimizes the sum of squared errors.
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- **Polynomial Regression:** Captures non-linear relationships by fitting a polynomial curve to the data.
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- **[Multiple Linear Regression:](https://www.codecademy.com/learn/multiple-linear-regression-course):** Used when there are multiple input features influencing the target variable.
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- **[Support Vector Regression (SVR):](https://www.codecademy.com/resources/docs/sklearn/support-vector-machines):** Uses SVM principles to find the best-fitting hyperplane within a margin of error.
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- **[Decision Tree Regression:](https://www.codecademy.com/article/mlfun-decision-trees-article):** Uses a tree structure where nodes represent feature-based decisions, and leaves represent predicted values.
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- **[Random Forest Regression:](https://www.codecademy.com/learn/machine-learning-random-forests-decision-trees):** An ensemble method that combines multiple decision trees to improve prediction accuracy and reduce overfitting.
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- **Neural Network Regression:** Uses neural networks to learn complex non-linear relationships between features and the target variable.
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**Common Classification Algorithms:** Linear Regression, Polynomial Regression, Support Vector Regression (SVR), Decision Tree Regression, Random Forest Regression, Neural Network Regression.

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