Loss functions | basic | coding | Linear Regression | Machine Learning Course | part 2

Hi, welcome to another video of machine learning
This is part 2 In this video we have learnt about Loss and Cost functions in linear Regression.
I have explained all this with the help of animation. Animation by tool called manim.
Loss function tell the model how good fit the current line is so we use it to know if the line is good or not or inshort it helps to score values of theta or coefficients.

Loss function can easily be calculated by finding diff of predicted values and true value and at last squaring it.
Cost function the average of all the losses for all the points.

after this video you will know following topics:
what are loss functions
what are cost functions
what are true and predicted values
and at last coding them to get much familiar


timestamps:
0:00 - What to expect
0:55 - what is true and predicted value ( animation )
2:30 - What are loss functions ( animation )
4:26 - What are Cost functions ( animation )
7:35 - Setting up files
7:41 - All about spyder
8:48 - Coding loss and cost functions
14:20 - scattering loss with range of theta1 ( coding )
17:55 - outro

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