Types of Machine Learning

The different types of Machine Learning are shown in fig.

1. Supervised Learning- In this type of learning, the machine is provided with a given sit if inputs with their desired outputs. The machine needs to study those given sets of inputs and outputs and find a general function that maps inputs to desired outputs.
2. unsupervised Learning -This type of learning is termed as 'learned by is own' by discovering and adopting, based on the input pattern. In this learning, the data are divided into different clusters and hence the learning is called a clustering algorithm.
3. Semi-supervised  Learning - This learning is used for the same applications as supervised learning. But it uses both labeled and unlabeled data for training. This type of learning can be used with methods such as classification, regression, and prediction. Simi-supervised learning is useful when the cost associated with labeling is too high to allow for a fully labeled training process, Early examples of this include identifying a persin's face on a webcam.
4. Reinforcement Learning(RL) - In this type of learning, the machine is trained to make specific decisions based on the business requirement with the objective to maximize the efficiency (performance). This continual learning process ensures less participation of human expertise and saves more time. Reinforcement learning is often used for robotics, gaming, and navigation. With reinforcement learning, the algorithm discovers through trial and error which actions yield the greatest rewards.