What is Supervised Machine Learning?

Supervised learning is a machine learning task where all data is labeled and we have input variables (x) and an output variable (Y) and use an algorithm to predict the output from the input data:

Y = f(X)

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What are the different types of supervised learning in machine learning?

Supervised learning problems can be further grouped into regression and classification problems.

• Classification: a classification problem is when the output variable is a category: "red" or "green", "yes" or "no", "true" or "false".
• Examples of predictions using classification: 
• spam detection
• churn prediction
• sentiment analysis
• Regression: a regression problem is when the output variable is a real value, such as "dollars" or "weight"
• Examples of predictions using classification: 
• house price prediction
• stock price prediction

Which are the most used supervised machine learning algorithms?

The most widely used supervised learning algorithms are:

• Support Vector Machines
• Linear Regression
• Logistic Regression
• Naive Bayes
• Linear Discriminant Analysis
• Decision Trees
• K-Nearest Neighbor Algorithm
• Neural Networks (Multilayer perceptron)
• Similarity Learning

Resources:

1. Supervised and Unsupervised Machine Learning Algorithms
2. Supervised learning
3. Supervised Machine Learning: Classification