Supervised learning is a type of machine learning in which models are trained using labeled data. In supervised learning, the algorithm learns from the training data and creates a mapping function that relates input variables to output variables. The goal of supervised learning is to predict the output based on new, unseen input data.
In supervised learning, the training data consists of input-output pairs, where the input is the independent variable and the output is the dependent variable. The algorithm learns to predict the output by finding the relationship between the input and output variables in the training data.
There are two main types of supervised learning: classification and regression. In classification, the output variable is a category or class label, while in regression, the output variable is a continuous value.
To implement supervised learning in machine learning models, you can use various algorithms such as linear regression, logistic regression, support vector machines, decision trees, random forests, and neural networks.
Now, let’s create a simple HTML tutorial to explain supervised learning using some basic HTML tags:
<!DOCTYPE html>
<html>
<head>
<title>Supervised Learning Tutorial</title>
</head>
<body>
<h1>What is Supervised Learning?</h1>
<p>Supervised learning is a type of machine learning where models are trained using labeled data.</p>
<h2>Types of Supervised Learning</h2>
<ul>
<li><strong>Classification:</strong> Predicting the class label of new data points.</li>
<li><strong>Regression:</strong> Predicting a continuous value for new data points.</li>
</ul>
<h2>Algorithms for Supervised Learning</h2>
<ul>
<li>Linear regression</li>
<li>Logistic regression</li>
<li>Support vector machines</li>
<li>Decision trees</li>
<li>Random forests</li>
<li>Neural networks</li>
</ul>
<h2>Benefits of Supervised Learning</h2>
<p>Supervised learning models can make accurate predictions on new data by learning from labeled training examples.</p>
<h2>Conclusion</h2>
<p>Supervised learning is a powerful tool in machine learning that enables us to build models that can predict outcomes based on labeled data. With the right algorithms and techniques, supervised learning can be applied to a wide range of real-world problems.</p>
</body>
</html>In this HTML tutorial, we have explained the concept of supervised learning, its types, algorithms, and benefits. You can customize this tutorial further by adding more details or styling using CSS. Feel free to experiment and enhance this tutorial to make it more informative and engaging.
