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Previously, we talked about how to build a binary classifier by implementing our own logistic regression model in Python. In this post, we're going to build upon that existing model and turn it into a multi-class classifier using an approach called one-vs-all classification.
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Logistic regression is a simple classification method which is widely used in the field of machine learning. Today we’re going to talk about how to train our own logistic regression model in Python to build a a binary classifier. We’ll use NumPy for matrix operations, SciPy for cost minimization, Matplotlib for data visualization and no machine learning tools or libraries whatsoever.

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Learning curves are very useful for analyzing the bias-variance characteristics of a machine learning model. In this post, I’m going to talk about how to make use of them in a case study of a regression problem. We’re going to start with a simple linear regression model and improve it as much as we can by taking advantage of learning curves.

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Hey everyone, welcome to my first blog post! This is going to be a walkthrough on training a simple linear regression model in Python. I’ll show you how to do it from scratch, without using any machine learning tools or libraries. We’ll only use NumPy and Matplotlib for matrix operations and data visualization.

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