This tutorial is on the basics of applying logistic regression, using a little bit of Python. It is also a continuation of the post “What is Linear Regression?”, which can be found here.

It is a little counterintuitive, but Logistic Regression is typically used as a classifier. In fact, Logistic Regression is one of the most used and well-known classification methods Data Scientists use. The idea behind this classification method is that the output will be between 0 and 1. Essentially returning the probability that the data you gave to the model, belongs to a certain group or class. …

This tutorial is on the basics of linear regression. It is also a continuation of the Intro to Machine Learning post, “What is Machine Learning?”, which can be found here.

Linear regression is one of the fundamental tools for data scientists and machine learning practitioners. We reference the equation `y = mx + b`

in the "What is Machine Learning?" post, which can be thought of as the basis of linear regression. Linear regression models are highly explainable, quick to train, can provide insights into your data, and be further optimized to make better predictions. …

This tutorial is on the basics of gradient descent. It is also a continuation of the Intro to Machine Learning post, “What is Machine Learning?”, which can be found here.

Gradient descent is a method of finding the optimal weights for a model. We use the gradient descent algorithm to find the best machine learning model, with the lowest error and highest accuracy. A common explanation of gradient descent is the idea of standing on an uneven baseball field, blindfolded, and you want to find the lowest point of the field. Naturally, you will use your feet to inch your…

Machine learning, put simply, is the ability to use algorithms and mathematics to find patterns in data. You’ll find machine learning applied to time-series data (think stock exchange data), language, images, etc. All machine learning is really doing, is finding mathematical representations within the data that you fed it. These algorithms, trained on data, can then be saved somewhere and are referred to as models. The models model the data, creating a generalized representation.

For example, if we feed an algorithm designed to classify fruit, we can send this algorithm images of apples and oranges. We also label the images…

Writing (actual pen to paper writing) can be quite therapeutic. Recently, making sure to slot time during the week for the sole purpose of reading and writing has shown to be quite relaxing. Doing so can potentially increase creativity. While writing, there is a noticeable jump in ideas and creativity once the pen lets loose on the paper. However, the hardest part is actually being able to come up with ideas to write about in the first place.

After doing this for a few weeks, I’ve noticed there are some environments more suitable than others. I’ve sat down to write…

Senior AI Scientist for Target. Founder of Data Science Minneapolis. Member of the Matrix Profile Foundation. I write about tech and artificial intelligence.