• Supervised Learning: Supervised learning is the easiest type of machine learning. It is used to train the machine with labeled data. Labeled data is a group of samples that have been tagged with one or more labels (information tags). The labeled data is fed to the machine one by one until the machine can recognize the data on its own. It is just like a teacher trying to teach a kid all kinds of labeled cards in a deck of cards one by one. The data itself is the teacher in supervised learning.
• Unsupervised Learning: Unsupervised learning is, interestingly, the opposite of supervised learning. It is used for data with without labels or information tags. The algorithm is fed with a lot of data and tools to understand the properties of data. The machine will organize the data in clusters, classes, or groups in such a way that it can make sense. Taking a huge amount of random data as an input and making sense out of it is what makes this learning model brilliant.
• Reinforcement learning: The reinforcement learning model from the above-mentioned learning models. It is a kind of model which learns from its mistakes. When we place a reinforcement learning model in any environment, it makes a lot of mistakes. We provide a positive feedback signal when the model performs well and a negative feedback signal when it makes errors, to promote positive learning and make our model efficient.