The Basics:

Machine Learing can be divided into two parts Supervised(Task Driven) and UnSupervised(Data Driven):

Now Let’s Start with Linear Regression:

Linear Regression

• Our Aim : To Find the Best fit line or get the minimal error

OKay Now what about the Maths Behind it???

• Well it’s nothing but your very on favourithe formula of slope of a line $ y = mx + c $
• where m is the slope or coefficient
• and c is the intercept
• Now wee’ll also have a function of hypothesis let’s see the function:
• Now we have also seen the cost function and so our main goal would be to minimize that

The Gradient Descent

• If we move towards a negative gradient or away from the gradient of the function at the current point, it will give the local minimum of that function.
• Whenever we move towards a positive gradient or towards the gradient of the function at the current point, we will get the local maximum of that function.
• The main objective of using a gradient descent algorithm is to minimize the cost function using iteration.

So the Outline will be