|
| 1 | +--- |
| 2 | +Title: 'Supervised Learning' |
| 3 | +Description: 'Supervised learning is a machine learning technique where algorithms learn from labeled data to make predictions.' |
| 4 | +Subjects: |
| 5 | + - 'AI' |
| 6 | + - 'Data Science' |
| 7 | + - 'Machine Learning' |
| 8 | +Tags: |
| 9 | + - 'AI' |
| 10 | + - 'Deep Learning' |
| 11 | + - 'Classification' |
| 12 | + - 'Regression' |
| 13 | +CatalogContent: |
| 14 | + - 'learn-python-3' |
| 15 | + - 'paths/computer-science' |
| 16 | +--- |
| 17 | + |
| 18 | +**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. |
| 19 | + |
| 20 | +**Examples:** Identifying handwritten digits, predicting car prices based on features, detecting spam emails based on content and metadata. |
| 21 | + |
| 22 | +### Key Components |
| 23 | + |
| 24 | +- **Training Data:** A dataset containing input-output pairs (e.g., images labeled with digits or emails marked as spam/not spam). |
| 25 | +- **Model:** A machine learning algorithm (e.g., decision trees, neural networks) that learns patterns from the data. |
| 26 | +- **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). |
| 27 | +- **Optimization:** A process of adjusting model parameters to minimize the loss and improve accuracy, often using gradient descent or other optimization techniques. |
| 28 | + |
| 29 | +## Types of Supervised Learning |
| 30 | + |
| 31 | +### Classification |
| 32 | + |
| 33 | +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. |
| 34 | + |
| 35 | +**Examples:** Spam Detection, handwritten digit recognition, image classification, medical diagnosis. |
| 36 | + |
| 37 | +#### Types of Classification |
| 38 | + |
| 39 | +- **Binary Classification:** The task of classifying data points into one of two classes. |
| 40 | +- **Multi-class Classification:** The task of classifying data points into one of more than two classes. |
| 41 | +- **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. |
| 42 | + |
| 43 | +**Common Classification Algorithms:** Logistic Regression, Support Vector Machines (SVMs), Decision Trees, Random Forests, Naive Bayes, K-Nearest Neighbors (KNN) |
| 44 | + |
| 45 | +### Regression |
| 46 | + |
| 47 | +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. |
| 48 | + |
| 49 | +**Examples:** House price prediction, stock price prediction, temperature forecasting, sales forecasting. |
| 50 | + |
| 51 | +#### Types of Regression |
| 52 | + |
| 53 | +- **[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. |
| 54 | +- **Polynomial Regression:** Captures non-linear relationships by fitting a polynomial curve to the data. |
| 55 | +- **[Multiple Linear Regression:](https://www.codecademy.com/learn/multiple-linear-regression-course):** Used when there are multiple input features influencing the target variable. |
| 56 | +- **[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. |
| 57 | +- **[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. |
| 58 | +- **[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. |
| 59 | +- **Neural Network Regression:** Uses neural networks to learn complex non-linear relationships between features and the target variable. |
| 60 | + |
| 61 | +**Common Classification Algorithms:** Linear Regression, Polynomial Regression, Support Vector Regression (SVR), Decision Tree Regression, Random Forest Regression, Neural Network Regression. |
0 commit comments