Supervised learning

How it works: Supervised learning is the simplest form of learning mode, where the ML algorithm is presented with labeled data (data with known outcomes). These data are used to adjust parameters and uses the outcomes to refine these parameters. The algorithm is then tested without seeing the outcomes and compared to the actual outcomes to ensure the accuracy. In supervised learning, we model relationships between the input features and target outcome such that we can predict the outcome values for new data based on those relationships which the algorithm learnt from previous data. Supervised learning process is similar to function approximation, where using an algorithm, we pick the function that best describes the input data. However, oftentimes we are not able to find the exact function describing the data. Also, algorithms rely upon assumptions made by humans about how the computer should learn. These assumptions and model inaccuracy introduces bias, which we will cover in section xxx. 
Common algorithms: The main types of supervised learning problems include regression and classification problems. Some common supervised learning algorithms include nearest neighbor, Naive Bayes, decision trees, linear regression, support vector machines, and deep learning.