Branch of Machine Learning dealing with Supervised Learning.
Something that isn’t talked about enough but silently haunts all machine learning practitioners. Data leakage. While data leakage is rarely discussed, I’ve seen it destroy production models. This blog post will discuss detecting and preventing data leakage in machine learning…
We’ve all been there, cleaned up our dataset, and realized it’s incredibly noisy. Should you get rid of the noise? Why is it even there? And what even is noise? In this blog post, we’ll look at what noise is,…
Bootstrapping is a resampling method that is used in machine learning. It is a widespread technique due to its flexibility since it does not require anything other than your training dataset (that I presume you already have). This blog will…
Epochs in machine learning can be confusing for newcomers. This guide will break down epochs and explain what they are, how they work, and why they’re important. We’ll also dive deep into Epochs’ relationship with Batch size and iterations. Deep…
Data is the heart and soul of machine learning. Without data, data scientists cannot create impactful machine-learning algorithms (duh). While this seems pretty standard, the type of data makes a huge difference when trying to perform data analysis. And sometimes, when we…
Machine learning is a vast and complex field that covers many different concepts. While there is some jargon to get up and running in machine learning, some of the ideas behind this jargon are very simple. This guide will focus…
Machine learning is a vast and complex field that has inherited many terms from other places all over the mathematical domain. It can sometimes be challenging to get your head around all the different terminologies, never mind trying to understand…
As I’m sure you know, Machine learning is the process by which a computer system can learn from past events to recognize patterns to predict the future. With this comes some dense math and some exciting concepts. In machine learning,…
Instance-based learning is a machine learning technique that relies on storing and recalling instances or examples of training data. You may have also heard of instance-based learning, referenced as memory learning or lazy learning. This is for a good reason,…
One common task you will encounter while working in machine learning is clunky datasets that need cleaning. This involves dealing with inconsistencies and errors in the data and data that wouldn’t “make sense” for our models in their current form.…