SUPERVISED MACHINE LEARNING

When is supervised learning used?

Supervised learning is commonly used when there is a known set of input-output pairs available for training, and the goal is to predict the output for new, unseen inputs. It has been successfully applied to various tasks, including image classification, speech recognition, spam detection, and many others.

Where is supervised learning used?

Supervised learning can be applied in various domains and industries. It is used in areas such as healthcare for diagnosing diseases, finance for predicting stock prices, customer service for sentiment analysis, and autonomous vehicles for object recognition and decision-making, among many others.

Who uses supervised learning?

Supervised learning is used by data scientists, machine learning engineers, and researchers working in academia and industry. These professionals develop and apply supervised learning algorithms to solve specific problems and improve decision-making in various domains.

Why is supervised learning important?

Supervised learning is important because it allows us to train models to make predictions or decisions based on available labeled data. It enables us to automate tasks, gain insights from large datasets, and improve efficiency and accuracy in decision-making processes.

How does supervised learning work?

In supervised learning, the algorithm learns from labeled training data by finding patterns and relationships between the input variables and their corresponding output variables. It builds a model that can generalize from the training data to make predictions on new, unseen data.

How many types of supervised learning are there?

There are mainly two types of supervised learning algorithms:

• Classification: The goal is to predict a discrete class label, such as categorizing emails as spam or non-spam.
• Regression: The goal is to predict a continuous numerical value, such as predicting house prices based on features like size and location.