Multivariable Linear Regression with Gradient Descent Algo - Concept and Implementation from scratch

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Implementation of Linear Regression using Gradient descent algorithm with two feature variables from scratch. Detailed concept of Multi variable linear Regression, gradient descent algorithm, the loss function, mean squared loss and how it works, updating weights, Learning rate, derivatives of loss function to find slope to reach minima. Vectorized code using numpy to avoid multiple for loops.

Video Sections:

0:00 - Intro
02:36 Concept Linear Regression
08:19 Concept Gradient Descent, Mean squared Loss, Derivative, weights update, learning rate, pseudocode
20:46 Implementation of Linear Regression
22:30 Standardization of variables
23:06 Concatenation for Vectorized Code
27:38 Gradient Descent Code
35:51 Gradient Descent Code
44:11 Plotting cost function
45:12 Animation Generation

Acknowledgements:

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Im a noob when it comes to machine learning, but youre so good at explaining things. This is by far the best basics tutorial. P. S love the robot thing 😂

nymamalik

Great video! I am finally able to relate the concent
But the code doesn't work for me
It shows:
Final values of coefficeints: [nan nan nan]

If I put, coeff = past_coeff[-1]
only then it return some final coefficients.

Can you please share the latest updated code and csv which works for you ?

freestyler

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