Explain Types of Machine Learning

Machine learning can be subdivided intothe main three types:

  • Supervised learning:
    Supervised learning is a type of machine learning in which machine learn from known datasets (set of training examples), and then predict the output. A supervised learning agent needs to find out the function that matches a given sample set.
    Supervised learning further can be classified into two categories of algorithms:
    1. Classifications
    2. Regression
  • Reinforcement learning:
    Reinforcement learning is a type of learning in which an AI agent is trained by giving some commands, and on each action, an agent gets a reward as a feedback.Using these feedbacks, agent improves its performance.
    Reward feedback can be positive or negative which means on each good action, agent receives a positive reward while for wrong action, it gets a negative reward.
    Reinforcement learning is of two types:
    1. Positive Reinforcement learning
    2. Negative Reinforcement learning
  • Unsupervised learning:
    Unsupervised learning is associated with learning without supervision or training. In unsupervised learning, the algorithms are trained with data which is neither labeled nor classified. In unsupervised learning, the agent needs to learn from patterns without corresponding output values.
    Unsupervised learning can be classified into two categories of algorithms:
    1. Clustering
    2. Association