Machine Learning Models:

Supervised Learning:

Involves a series of function that map's an to an output based on a series of example input-output pairs.

Eg: We have 2 variables Age(Input) and Shoe size(Output) we can predict shoe size of people acc to age using supervised leaning model

SUPERVISED LEARNING there are two sub categories one is REGRESSION and other is CLASSIFICATION

1. Regression Model:

Find relationship between a dependent Variable and Independent Variable

Here OUTPUT is always CONTINOUS.

Types:

1.1 Linear Regression:

Finding a curve/ line for best fit

Right answer is on the red line and blue dots are the predictions made by model

1.2 Decision Tree:

To create a training model that can use to predict the class or value of the target variable by learning simple decision rules inferred from prior data(training data).

1.3 Random Forests:

Random Forest are so called because each tree in the forest is built by randomly selecting a sample of the data

Relies on majority wins model it reduces the risk of error from individual tree.

For example, the prediction for tress 1 and 2 is apple. Another decision tree (n) has predicted banana as the outcome. The random forest classifier collects the majority voting to provide the final prediction. The majority of the decision trees have chosen apple as their prediction

1.4 Neural Network:

Using algorithms, they can recognize hidden patterns and correlations in raw data, cluster and classify it, and – over time – continuously learn and improve.

Is a multi layered model inspired by human minds like neurons in our brain the circle represents a node