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Linear regression is the simplest machine learning model that tries to find the relationship between two variables. The equation is:
In machine learning, we adjust the parameters mm (slope) and cc (intercept) so that the line best fits the data.
Loss functions tell us how well our model is performing. A commonly used loss function is Mean Squared Error (MSE), which measures the difference between predicted and actual values.
- Mean Squared Error (MSE):
Machine learning is powered by fundamental mathematics, especially concepts like linear algebra, calculus, and probability. By understanding these areas, you’ll have a solid foundation to dive deeper into more complex machine learning algorithms. Start with the basics, and the rest will begin to fall into place.
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