Introduction to Machine Learning: K Means

Welcome to our fourth installment on Machine Learning. In this module we’re going to cover K-Means. K-Means is a clustering algorithm based on the hyperparameter “K” which dictates how many clusters there will be. A hyperparameter is just a parameter that we can adjust. Each cluster has a “centroid” or a central point that willContinue reading “Introduction to Machine Learning: K Means”

Introduction to Machine Learning: Logistic Regression

Unlike linear regression, logistic regression is used for classification rather than prediction along a continuous range. The secret sauce to logistic regression is an “activation function” that scores the independent variable(s) and returns a 0 if the resulting score is below threshold and 1 if the resulting score is above threshold. It can be usedContinue reading “Introduction to Machine Learning: Logistic Regression”

Introduction to Machine Learning: Linear Regression

Linear Regression is a technique to create a linear equation given a dataset. We use this when we expect to have a linear correlation, perhaps something like square footage of an apartment compared to rent price. First, I’m going to show you an example of how linear regression works via sklearn and then we’ll buildContinue reading “Introduction to Machine Learning: Linear Regression”

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