1. Supervised Learning Supervised Learning input is provided as a labelled dataset, a model can learn from it to provide the result of the problem easily. 2. Unsupervised learning Unsupervised learning is self-organized learning. Its main aim is to explore the underlying patterns and predicts the output. 3. Reinforcement Learning It is neither based on supervised learning nor unsupervised learning. Moreover, here the algorithms learn to react to an environment on their own. It is rapidly growing and moreover producing a variety of learning algorithms. These algorithms are useful in the field of Robotics, Gaming