Machine_Learning-

Supervised and unsupervised machine learning

Supervised machine learning	Unsupervised machine learning
supervised machine learning algorithum are trained using label data	unsupervised machine learning alorithum are traind using unlabeled data
Model takes direct feedback to check whether it is predicting correct output or not	Model does not take any feedback
Model predicts the output	Model does not take any feedback
Input data provided to model along with output	Only input data is provide to the model
GOAL : To train model so that it can predict the output when it given new data	GOAL: To find the hidden pattern & useful insights from the unknown dataset
Need supervision to train the model	Does not need supervision to train model
Used for those cases where we know the input as well as corresponding output data	Used for those cases where we know the input data and no corresponding output data
Catogerical in regression and classification	Classification in clustering and association
Produce accurate result	Model may give less accurate result as compared to supervised learning
SL is not close to AI as in this case, we first train the model for each data & then only it can predict the correct output	More close to true AI as it learn similarity as a child learns daily routine things by his experinces
Include algorithum : Regression, Decision Tree , SVM, Multi-class Classification , K-Neighbour , Logistic Regression.,etc	Include algorithum : Clustering, KNN, Apriori Algorithum.,etc

                     Reniforcement Learning is a feedback based learning method.

In which a Learning agents gets a reward for each right action and get penalty for each wrong action .
The agent learns automatically with these feedbacks and improves its performance.
In reniforcement learning, the agent intracts with the enironment and explores it
The goal of an agent is to get the most reward points, and hence , it improves its performances
Example :The robotic dog, which automatically learns the movement of his arms.