What is the difference between supervised and unsupervised learning algorithms?
Machine learning is classified into 4 learning algorithms:

Supervised learning algorithms

Unsupervised learning algorithms

Reinforcement Learning algorithms

Evolutionary Learning algorithms
Difference between supervised and unsupervised learning algorithms: 
Supervised learning algorithms: These algorithms require the knowledge of the both outcome variable (dependent variable) and the features (independent variable or input variable. The algorithm learns how to estimate the value of the model parameters by defining the function which is usually a function of the difference between predicted value and actual value of the outcome variable. Algorithms such as linear regression, logistic regression, discriminant analysis are some examples of supervised learning algorithms

Unsupervised learning algorithms: These algorithms are the set of algorithms which do not have the knowledge of outcome variable in the dataset. The algorithm must find the possible value of the outcome variable. Algorithms such as clustering, principal component analysis (PCA) are examples for Unsupervised learning algorithms.
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