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Matrix form of the closed-form solution for the hypothesis function

The cost function J(θ)J(theta) is a mathematical measure of how well a machine learning model’s predictions match the actual target values. It evaluates the overall error between predicted outputs hθ(x)h_theta(x) and true outputs yy across the entire dataset. For example, in linear regression, the cost function is typically the Mean Squared Error (MSE): J(θ)=12m∑i=1m(hθ(x(i))−y(i))2 Where:

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Transition from slope intercept to matrix form

As we move from simple linear equations like y=mx+by = mx + b to models with multiple variables, things become more complex. To handle these higher-dimensional problems efficiently, we need matrix representation. Matrices allow us to perform calculations with multiple features simultaneously, making the process faster and more scalable. By using matrix form, we can

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Introduction – Linear Regression

Introduction https://youtu.be/Z8mOZ-STNnQ Linear regression is one of the simplest and most commonly used methods for predicting outcomes or understanding relationships between two variables. For example: In a business setting, you might use it to predict sales based on advertising spending. In health care, you could predict a patient’s recovery time based on the dosage of

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