This is part ten of a series I’m working on, in which we’ll discuss and define introductory machine learning algorithms and concepts. At the very end of this article, you’ll find all the previous pieces of the series. I suggest you read these in sequence. Simply because I introduce some concepts there that are key to understanding the notions discussed in this article, and I’ll be referring back to them on numerous occasions.

In this article, we’ll quickly review the Gaussian distribution, define what an anomaly is, and how we detect them.

Let’s get right into it.

Formally, an anomaly…

If you’re anything like me, and you’re just getting started in your machine learning career, then you’ve probably stumbled upon the concepts of precision and recall. These terms often come up when studying the notion of model evaluation.

More often than not, beginners struggle to understand these concepts. Not because they’re difficult, but because the same confusing techniques and definitions are used time and time again. Normally, the concept will be explained by first defining the terms, then showing a real-world example of model evaluation. Let’s look at a few examples. …

This is part nine of a series I’m working on, in which we’ll discuss and define introductory machine learning algorithms and concepts. At the very end of this article, you’ll find all the previous pieces of the series. I suggest you read these in sequence. Simply because I introduce some concepts there that are key to understanding the notions discussed in this article, and I’ll be referring back to them on numerous occasions.

Today we look at the importance of dimensionality reduction, as well as the principal component analysis (PCA) algorithm, the most widely used algorithm for dimensionality reduction.

Let’s…

This is part eight of a series I’m working on, in which we’ll discuss and define introductory machine learning algorithms and concepts. At the very end of this article, you’ll find all the previous pieces of the series. I suggest you read these in sequence. Simply because I introduce some concepts there that are key to understanding the notions discussed in this article, and I’ll be referring back to them on numerous occasions.

Today we look at our very first (and only) unsupervised learning algorithm. …

This is part seven of a series I’m working on, in which we’ll discuss and define introductory machine learning algorithms and concepts. At the very end of this article, you’ll find all the previous pieces of the series. I suggest you read these in sequence. Simply because I introduce some concepts there that are key to understanding the notions discussed in this article, and I’ll be referring back to them on numerous occasions.

Today we look at our very last supervised learning algorithm, support vector machines (SVMs). …

This is part five of a series I’m working on, in which we’ll discuss and define introductory machine learning algorithms and concepts. At the very end of this article, you’ll find all the previous pieces of the series. I suggest you read these in sequence. Simply because I introduce some concepts there that are key to understanding the notions discussed in this article, and I’ll be referring back to them on numerous occasions.

Up to this point, we’ve looked at data preprocessing, as well as three **supervised learning **algorithms. Namely, linear regression, logistic regression, and neural networks. …

This is part five of a series I’m working on, in which we’ll discuss and define introductory machine learning algorithms and concepts. At the very end of this article, you’ll find all the previous pieces of the series. I suggest you read these in sequence. Simply because I introduce some concepts in previous posts that are key to understanding neural networks, and I’ll be referring back to them on numerous occasions.

In the previous article, we went through the basics of neural networks. We looked at the theory, as well as some basic terminology.

In this article, we continue with…

If you were born any time before the 2000s, then the experience of browsing through rows upon rows of movies at Blockbuster, looking for that *one *movie that you just *know *will make the best night, isn’t a foreign one to you. It is? Well, what about Limewire? Chapters? If none of these terms spark a warm feeling in your stomach, well then, hello there generation z-er.

Society has drastically changed, for better or for worse, since. With the rise of Netflix, Spotify, Amazon, and other big companies, we now live in a world where your machine most likely knows…

This is part four of a series I’m working on, in which we’ll discuss and define introductory machine learning algorithms and concepts. At the very end of this article, you’ll find all the previous pieces of the series. I suggest you read these in sequence. Simply because I introduce some concepts there that are key to understanding neural networks, and I’ll be referring back to them on numerous occasions.

In this article, we go through the theory behind neural networks, as well as introduce the motivation behind such a model and a few definitions. …

This is part three of a series I’m working on, in which we’ll discuss and define introductory machine learning algorithms and concepts. At the very end of this article, you’ll find all the previous pieces of the series. I suggest you read Linear Regression: Intuition and Implementation before you dive into this one. Simply because I introduce some concepts there that are very relevant to logistic regression, and I’ll be referring back to them on numerous occasions.

In this article, we go through the theory behind logistic regression, as well as see it in action using Scikit-Learn’s logistic regression class.

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Software engineering student with a love for everything applied mathematics