As a headline note, I am not writing these staff to give all the details and the information about the headings, also I am not qualified as this much. I just trying to underline some basic facts for the ones who are interested in machine learning and searching some facts to investigate. Thus my headings just small introduction for your ML world search.

**Linear Regression: **a basic algorithm to estimate continuous output value by considering the attributes of the given instance according to the given instances in data-set with their attributes and the output values. It is like saying; You have two data as x is attribute, y is output-> (2,4), (4,8) and there is given (3,?) waiting for estimation to y=?. The basic intention gives the answer by using ratios as (3,6). Linear regression is such a basic algorithm doing such notions in itself. For more…

**Cost Function:** is the functions defines the error in one of the specific algorithm and it varies relative to the algorithm in use. For more…

**Gredient Descent:** is the way of optimization of cost function. In other words, it is one of the ways of getting necessary values for your machine learning algorithm gets less and less error that is less cost. The basic notion of the Gradient Decent is to decrease the result of the Cost function by finding its slope (the direction of the function increases) by taking derivative and negate the slope (the direction of the function decreases) and go with that negated slope’s direction on function that is the way of decreases the cost function. for more…