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Linear Regression from Scratch

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Build linear regression with only NumPy.

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me tuning hyperparameters

gradient descent

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ML from Scratch

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ML from Scratch

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What is Machine Learning?

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Setting Up Python + Jupyter

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Linear Regression from Scratch

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Neural Networks: Basics

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Gradient Descent Visualized

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Overfitting & Regularization

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01

What is Machine Learning?

12:08

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02

Setting Up Python + Jupyter

7:24

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03

Linear Regression from Scratch

18:45

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04

Key formulas cheatsheet

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Overfitting & Regularization

15:32

Linear Regression from Scratch

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Build linear regression with only NumPy.

y = X @ w + b loss = MSE(y, y_hat) w -= lr * grad

Covers gradient descent and vectorized ops.

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My Notes

Need to remember the vectorized update for weights and bias.

w -= lr * grad_w b -= lr * grad_b

Come back and compare this with the author's explanation of gradient descent.

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ML from Scratch

6 items

2/6 done33%

Linear Regression from Scratch

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18:45

Author's notes

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This lesson introduces linear regression, loss minimization, and how gradient descent updates the weights.

prediction = X @ w + b loss = mse(prediction, y)

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