Every data scientist should have SVM in their toolbox. Learn how to master this versatile model with a hands-on introduction. — Among the available Machine Learning models, there exists one whose versatility makes it a must-have tool for every data scientist toolbox: Support Vector Machine (SVM). SVM is a powerful and versatile algorithm, which, at its core, can delineate optimal hyperplanes in a high-dimensional space, effectively segregating the different classes of…

Learn how companies like Facebook and LinkedIn extract insights from networks — How do streaming platforms like Netflix recommend the perfect movie for your evening? How can we optimize player positioning in sports like soccer? How can we identify fraudulent activities in financial networks? How can we predict a match in dating apps like Tinder? All these challenges have the same solution…

Master computer vision by building a CNN from scratch all by yourself — As Computer Vision applications are now present everywhere in our daily lives, it is fundamental for every Data Science practitioner to understand their functioning principles and familiarize themselves with them. In this article, I built a Deep Neural Network without relying on popular modern deep learning libraries like Tensorflow, Pytorch…

Learn how to efficiently apply state-of-the-art Dimensionality Reduction methods and boost your Machine Learning models. — When approaching a Machine Learning task, have you ever felt stunned by the massive number of features? Most Data Scientists experience this overwhelming challenge on a daily basis. While adding features enriches data, it often slows the training process and makes it harder to detect hidden patterns, resulting in the…

The Curse of Dimensionality can be tamed! Learn how to do it with Python and Scikit-Learn. — In the novel Flatland, characters living in a two-dimensional world find themselves perplexed and unable to comprehend when they encounter a three-dimensional being. I use this analogy to illustrate how similar phenomena occur in Machine Learning when dealing with problems involving thousands or even millions of dimensions (i.e. …

How to choose it and minimize your neural network training time. — Developing any machine learning model involves a rigorous experimental process that follows the idea-experiment-evaluation cycle.

Improve your Data Science portfolio with hands-on projects based on 10 real-world datasets. — The Machine Learning field is rapidly advancing, leading practitioners to crave for real-world datasets to refine their algorithms and test their skills. However, the search for suitable datasets often comes with non-trivial challenges. …

Coding and explaining the working principles of Optical Character Recognition through the popular algorithm K Nearest Neighbors — Optical Character Recognition (OCR) is present in our daily life more regularly than we imagine. When we use Google Translate to translate text from pictures, we’re using OCR. When we send a letter and it arrives at its destination, OCR is working for us. When a visually impaired person scans…

Can a language model invent unique fantasy character names? Let’s build it from scratch — To truly grasp the intricacies of Language Models (LM) and become familiar with their underlying principles, there is no other way than rolling up our sleeves and starting to write code. …