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.
- 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 -
- 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.
- 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
- 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
- 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