Linear Regression with one variable - Introduction

 It is not but making a some how clear relationship among variables

the dependent and independent variables.

talking in terms of maths the equation can be used meaningfully for something

may be to determine /predict values from data.

if y = m * x + b 

the values for m , b can be anything but has to appropriate to predict y 

so the loss which is  difference from existing to prediction is close to zero ~0

to start with we can say the one variable as -x 

in some scenario m , b are called variables 

 

the equation stated about is a line equation we have any equation 

y = 2*x 

y = x*x

y = 2x +2x*x 

so why the need of all these equations , it is all about playing data now a days

in machine learning problems we create a data sets , lets consider as x 

y to be a value of x the datas .

y = datas 

when we express the data as a function and plot in the graph we get the curves 

take some random data x and plot x and y 

x =1 , 2, 3 ,4 

y = x 

the equation we have formed here is whatever the value of x the same for y ,plotting that in graph , dont worry jus try to understanding these things ,going forward will prove the relation to real time machine learning problems.

ok consider another example 

y = x + b adding a constant b 

x = 1 ,2 ,3, 4

b = 1

y = 2, 3 , 4, 5  plotting in graph 

if y = w1 * x1 + w2 * x2 + b

here x1 ,x2 are 2 variables let say which is called Linear regression multiple variables  which we will see later.