If you are here reading this post, you are well aware of the importance of machine learning. Prevalence of machine learning has been increasing tremendously in the recent years due to the high demand in many business areas and the advancements in technology. There is a wide variety of machine learning algorithms that can be grouped in three main categories:

• Supervised learning algorithms model the relationship between features (independent variables) and a label (target) given a set of observation. Then the model is used to predict the label of new observations using the features.
• Unsupervised learning algorithms tries to find the structure in unlabeled data.
• Reinforcement learning works based on an action-reward principle. An agent learns to reach a goal by iteratively calculating the reward of its actions.
  In this post, I will give you an overview of supervised machine learning algorithms that are commonly used. Supervised learning algorithms try to predict a target (dependent variable) using features (independent variables). Depending on the characteristics of target variable, it can be a classification (discrete target variable) or a regression (continuous target variable) task. The algorithms we will cover:
• Linear Regression
• Support Vector Machines
• Naive Bayes
• Logistics Regression
• K-Nearest Neighbors
• Decision Trees
• Random Forests
• Gradient Boosted Decision Trees
  Let’s begin.

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