Welcome to the Getting Started guide for Supervised Learning. This page provides an overview of what supervised learning is, its importance, and how to get started with it.
What is Supervised Learning?
Supervised learning is a type of machine learning where the algorithm learns from labeled training data. The goal is to learn a mapping from input to output so that the model can make predictions on new, unseen data.
Key Components of Supervised Learning
- Input Data: This is the data that the model uses to make predictions.
- Output Data: This is the data that the model tries to predict.
- Training Data: This is a set of labeled data used to train the model.
- Test Data: This is a set of data used to evaluate the model's performance.
Importance of Supervised Learning
Supervised learning is crucial in many applications, such as:
- Image Recognition: Identifying objects in images, such as faces or vehicles.
- Medical Diagnosis: Predicting diseases based on patient data.
- Financial Modeling: Predicting stock prices or credit risks.
Getting Started with Supervised Learning
To get started with supervised learning, follow these steps:
- Choose a Programming Language: Python is a popular choice for machine learning due to its simplicity and the availability of libraries like scikit-learn.
- Gather Data: Collect and preprocess the data you will use for training and testing.
- Choose a Model: Select a suitable machine learning model for your task. Common models include linear regression, logistic regression, and decision trees.
- Train the Model: Use the training data to train the model.
- Evaluate the Model: Test the model's performance on the test data.
Example
For example, if you are interested in image recognition, you might use a Convolutional Neural Network (CNN) model.
Further Reading
For more information on supervised learning, check out our comprehensive guide on Machine Learning Basics.