Types of Machine Learning - Supervised Learning

Supervised Learning is the most intuitive and widely used type of Machine Learning. In this approach, the algorithm is trained on labeled data—data where the input and the desired output are already known. This training allows the model to learn from past data and make accurate predictions on new data.

🔍 How It Works:

Imagine you’re training a model to predict house prices. You have a dataset that includes various features of houses (like size, number of rooms, and location) and the corresponding prices. The model learns the relationship between these features and the prices, enabling it to predict the price of a new house based on its features.

🔍 Real-World Applications:

Supervised Learning is used in a variety of applications, from spam detection in emails to credit scoring, where the model predicts whether a loan applicant is likely to default based on their financial history. It’s also crucial in healthcare, where models predict patient outcomes based on historical data.

Supervised Learning is powerful, but it’s just one approach to ML. Next, we’ll explore Unsupervised Learning and how it differs from the supervised approach.