Comparing supervised, unsupervised, and reinforcement learning in detail

As machine learning has three major sections, let's take a high level look at the major differences and similarities:

• Supervised learning: In supervised learning, we have a training set for which we have given right answer for every training algorithm. The training example contains all the right answers, and the job of the training algorithm is to replicate the right answers.
• Unsupervised learning: In unsupervised learning, we have a set of unlabeled data and a learning algorithm. The job of the learning algorithm is to find the structure in the data with algorithms like k-means, PCA, and so on.
• Reinforcement learning: In reinforcement learning, we do not have a target variable. Instead we have reward signals, and the agent needs to plan the path on its own to reach the goal where the reward exists.