## Definition:

Supervised machine learning is a type of machine learning where the model is trained on a labeled dataset.

The labeled dataset consists of examples where each example is associated with a label.

The goal of supervised machine learning is to learn a mapping function from the input data to the corresponding labels.

This mapping function can then be used to make predictions on new, unseen data.

## Types of Supervised Machine Learning

**1. Classification: **Classification is the task of assigning a class label to an input data point.

Example: A classification model could be used to classify emails as spam or not spam, or to classify images of handwritten digits.

**2. Regression: **Regression is the task of predicting a continuous numerical value.

Example: A regression model could be used to predict the price of a house based on its size, location, and other features, or to predict the future sales of a product.

## Supervised Machine Learning Algorithms

Some of the most common algorithms include:

**1. Linear regression:** Linear regression is a simple and interpretable algorithm that can be used for both classification and regression tasks. It is a good choice for tasks where the relationship between the input data and the labels is linear.

**2. Logistic regression:** Logistic regression is a popular algorithm for classification tasks. It is a good choice for tasks where the labels are binary (e.g., yes/no, true/false).

**3. Support vector machines (SVMs): **SVMs are a powerful algorithm for both classification and regression tasks. They are known for their ability to handle high-dimensional data and their robustness to outliers.

**4. Decision trees: **Decision trees are a versatile algorithm that can be used for both classification and regression tasks. They are easy to interpret and can handle categorical data.

**5. Random forests: **Random forests are an ensemble algorithm that combines multiple decision trees to improve performance. They are a popular choice for classification and regression tasks.

## Applications of Supervised Machine Learning

**Spam filtering:**Can be used to filter spam emails from inboxes.**Medical diagnosis:**Can be used to diagnose medical conditions based on patient data.**Fraud detection:**Can be used to detect fraudulent transactions in financial data.**Customer segmentation:**Can be used to segment customers based on their demographics and behavior.**Recommendation systems:**Can be used to recommend products, movies, and other items to users.

## Steps in Supervised Learning:

**Data Collection**: Gather a dataset containing input features and corresponding target labels.**Data Preprocessing**: Clean, handle missing values, scale features, and split data into training and test sets.**Model Selection**: Choose an appropriate model or algorithm based on the problem and data characteristics.**Model Training**: The model learns from the training data by adjusting its parameters to minimize prediction errors.**Model Evaluation**: Assess the model’s performance using metrics like accuracy, MSE, precision, recall, etc., on a test set.**Model Tuning**: Fine-tune model hyperparameters or select a different algorithm if performance is inadequate.**Model Deployment**: Deploy the trained model to make predictions on new, unseen data in real-world applications.

References:

- Machine Learning: A Probabilistic Perspective by Kevin P. Murphy, MIT Press, 2012.
- Machine Learning: A Practical Guide by Florian Deisenroth, Faisal Abdulle, and Christopher Ong, Cambridge University Press, 2020.