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SimpleLinearRegression
Simple linear regression using Least Squares Criterion
y = mx + c
Finding m and c -
SimpleLinearRegressionGD
Simple linear regression using Gradient Descent
y = mx + c
Start with random slople(m) and constant(c)
Minimise the error through epocs
Finding m and c -
MultipleLinearRegression
Multiple linear regression
y = b0 + b1X1 + b2X2 +....+bn*Xn
Finding b0, b1 .... bn -
MultipleLinearRegressionGD
Multiple linear regression using Gradient Descent
y = b0 + b1X1 + b2X2 +....+bn*Xn
Start with random sloples(b1, b2.... bn) and constant(b0)
Minimise the error through epocs
Finding b0, b1 .... bn -
PolynomialRegression
Polynomial regression
y = b0 +(b1 * x^1)+(b2 * x^2)+...+(bn * x^n) $$
Finding b0, b1 .... bn -
Clustering
Given a datset of n dimension and no of clusters K to be formed
Form K clusters and allocate dataset samples to K clusters -
Neural Network
Classification using Neural Network
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