machine_learning

1. SimpleLinearRegression
   Simple linear regression using Least Squares Criterion
   y = mx + c
   Finding m and c
   

2. 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
   

3. MultipleLinearRegression
   Multiple linear regression
   y = b0 + b1X1 + b2X2 +....+bn*Xn
   Finding b0, b1 .... bn
   

4. 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
   

5. PolynomialRegression
   Polynomial regression
   y = b0 +(b1 * x^1)+(b2 * x^2)+...+(bn * x^n) $$
   Finding b0, b1 .... bn
   

6. 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
   

7. Neural Network
   Classification using Neural Network