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Logistic regression

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Unveiling the Mechanics of Logistic Regression Building upon the foundations of linear regression, we delve into the mechanics and mathematical underpinnings of Logistic regression. The underlying concept of logistic regression revolves around capturing the probabilistic relationship between input features and the binary output. This relationship is elegantly established through the logistic or sigmoid function, which smoothly maps the input space to a range between 0 and 1. This function allows us to interpret the output as the probability of belonging to a particular class. Imagine, a scenario where we are presented with a set of input features, such as the age and income of individuals, and our goal is to predict whether they are likely to purchase a particular product. Logistic regression steps up to the challenge by estimating the parameters—weights and biases—that maximize the likelihood of the observed data given the model's predictions. To get the optimal
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gradient descent

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Linear Regression Blog Post Gradient descent Introduction In our last article, we learned about the importance of the loss function and how lower loss values indicate better parameter values. Now, we will address the question of how to obtain these optimal parameters that minimize the loss. In this article, we will introduce the gradient descent algorithm, which will provide us with the solution. Imagine you are standing on a mountain and your goal is to reach the lowest point in the valley. You cannot see the entire landscape, but you can feel the slope of the ground beneath your feet. Gradient descent works in a similar way. Gradient descent allows us to find the optimal values for the model's parameters by iteratively adjusting them in the direction that decreases the loss. It relies on the concept of gradients, which represent the slopes of the loss function with respect to each parameter. By iteratively descending along the gradients, we gradually
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Linear regression

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Linear Regression Blog Post Linear Regression: Understanding the Fundamentals Linear regression is a fundamental technique in machine learning. It's a powerful tool that allows us to capture the relationship between variables by fitting a straight line to the data points. The beauty of linear regression lies in its simplicity and interpretability. We can directly observe how changes in the independent variables impact the dependent variable. It serves as a crucial starting point for understanding the principles of modeling and prediction. While linear regression may seem basic compared to more advanced models, it plays a pivotal role in building our understanding of more complex techniques, such as deep learning. By grasping the foundations of linear regression, we gain insight into the principles of optimization and minimizing errors. We estimate the coefficients that align the line with the data points, ensuring the best possible fit. Li
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