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…
When most people think about artificial intelligence, they automatically assume some machine learning goes on. After all, how could you have an AI system without machine learning? But what if there was a way to create AI without machine learning? …
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…
In the ever-evolving realm of data science and machine learning, the right tools are paramount to unleashing the full potential of innovation and discovery. At the heart of this technological frontier lies the processor—a critical component that serves as the…
There’s a lot of talk about data science and its prerequisites – what one needs to know to break in and become a data scientist. And rightfully so, given that the field is exploding with opportunities (and high salaries). But…
I remember when I first decided that I wanted to learn data science. I was fresh out of college and working a job as an engineer that I wasn’t in love with. I knew I wanted a change, but I…
Data science is one of the most influential and in-demand fields there is. While data scientists need to have a strong background in applied math and statistics, they also need to be able to program. Programming is a crucial knack…
Do you want to ace your next interview for a machine-learning role? If so, then you’ll probably need to practice with LeetCode. LeetCode is a website where you can practice coding questions for interviews. It is also a great place…
It’s a common misconception during model building that your goal is about getting the perfect, most accurate model on your training data. In reality, this is often not the case. Creating a model that knows the training data too well…
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…