Meaning of Supervised vs Unsupervised Learning vs Reinforcement Learning Supervised Learning Consider yourself as a student sitting in a classroom wherein your teacher is supervising you, “how you can solve the problem” or “whether you are doing correctly or not”. Likewise, in Supervised Learning input is provided as a labelled dataset, a model can learn from it to provide the result of the problem easily. Unsupervised Learning This learning algorithm is completely opposite to Supervised Learning. In short, there is no complete and clean labelled dataset in unsupervised learning. Unsupervised learning is self-organized learning. Its main aim is to explore the underlying patterns and predicts the output. Here we basically provide the machine with data and ask to look for hidden features and cluster the data in a way that makes sense. Example K – Means clustering Neural Networks Principal Component Analysis 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 etc. For a learning agent, there is always a start state and an end state. However, to reach the end state, there might be a different path. In Reinforcement Learning Problem an agent tries to manipulate the environment. The agent travels from one state to another. The agent gets the reward(appreciation) on success but will not receive any reward or appreciation on failure. In this way, the agent learns from the environment. Key Differences Between Supervised vs Unsupervised Learning vs Reinforcement Learning Criteria Supervised ML Unsupervised ML Reinforcement ML Definition Learns by using labelled data Trained using unlabelled data without any guidance. Works on interacting with the environment Type of data Labelled data Unlabelled data No – predefined data Type of problems Regression and classification Association and Clustering Exploitation or Exploration Supervision Extra supervision No supervision No supervision Algorithms Linear Regression, Logistic Regression, SVM, KNN etc. K – Means, C – Means, Apriori Q – Learning, SARSA Aim Calculate outcomes Discover underlying patterns Learn a series of action Application Risk Evaluation, Forecast Sales Recommendation System, Anomaly Detection Self Driving Cars, Gaming, Healthcare