Any machine learning algorithm is a hypothesis set which is taken before considering the training data and which is used for finding the optimal model. Machine learning algorithms have 3 broad categories –

Unsupervised learning — the dataset is unlabeled and the goal is to discover hidden relationships.

Reinforcement learning — some form of feedback loop is present and there is a need to optimize some parameter.

In this post, we will have a high-level description of some of the common and popular machine learning algorithms and have an elevated view of them. I will take up a more in-depth analysis of these algorithms in the future posts. Please note that this post builds up on my earlier post on common machine learning terms, so please take a look at that post before reading this.

Ordinary Least Squares Linear Regression

With linear regression, the objective is to fit a line through the distribution which is nearest to most of the points in the training set.

In simple linear regression, the regression line minimizes the sum of distances from the individual points, that is, the sum of the “Square of Residuals”. Hence, this method is also called the “Ordinary Least Square”.