Before starting lets dig into what is machine learning.

  • It is a type of Artificial Intelligence that allows software applications to learn from the data and become more accurate in predicting outcomes without human intervention.
  • In other words, it allows software apps to learn of its own by following a set of instructions.

 

SUPERVISED LEARNING

  • It is where we have an input variable(X) and output variable(Y) and we use an algorithm to learn the mapping function from the input to the output.
  • ALGORITHMS of SUPERVISED LEARNING -
  1. NAIVE BAYES CLASSIFIER
    • Based on Bayes Theorem where we have no relation between b/w different predictors.
    • Example -Probability of playing golf at different weather conditions.
  2. KNN (K-NEAREST NEIGHBORS)
    • An object is classified by a majority vote of its neighbor’s, or case based on a similarity measure.
    • Example –Search Results

 

UNSUPERVISED LEARNING

  • It is the training of model using information that is neither classified not labeled in which there is no explanation of the data.
  • It is also called "Clustering Analysis"
  • Example - Unlabeled picture or audio downloaded from internet

 

REINFORCEMENT LEARNING

  • It is an area of machine learning where an RL agent learns from the consequences of its actions, rather than being taught explicitly.
  • It selects the actions on the basis of its past experiences (exploitation) and also by new choices (exploration).
  • Here machine takes the decision on its own.

image reference : google 

table below explains all other types in detail