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  The progression from a simple linear equation to its matrix form and then to a hypothesis function is designed to make the relationship between inputs and outputs scalable and efficient. A basic linear equation describes how a single input influences an output, but in real-world problems, we often deal with multiple inputs and data […]

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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