7 skills to improve employability for a data science position — In this article, I will cover things that will help you land a data science job in 2022. If you meet all of these requirements, I believe you will be able to land a data science job fairly quickly. I did a comparable article a while back in which I…

Presenting 5 solutions that you can use to counteract overfitting in tabular data and classical machine learning — In this article, I will present an overview of solutions against overfitting, that apply to tabular data and classical Machine Learning. When comparing classical machine learning with deep learning, the latter is generally considered more complex. For the problem of overfitting, the contrary is the case: many tools and tricks…

Using a Genre Classification use case to learn how to apply Machine Learning to Sound and Audio data — In this article, you will learn the basics of doing Machine Learning on sound data. Sound data (often called audio data) is a data type that is not very intuitive to work with. At the end of this article, you will understand: how to convert sound files (mp3 or other)…

Learn what Fourier Transform is and how it can be used to decompose time series. With a worked Python example on CO2 time series data. — In this article, you will learn what a Fourier Transform is through one of its most intuitive applications: time series decomposition. You'll also see how to execute a Fast Fourier Transform using NumPy on a famous time series data set. The Fourier Transform The official definition of the Fourier Transform states that it…

An introduction to digital sound and audio data. With a Python example on exploring audio files, sound waves, and spectrograms. — Machine Learning and data analysis on sound is a growing domain with a huge potential for Data Science use cases. In this article, you will learn what sound is and how it can be represented on a computer. You will also learn what a Fourier Transform is and how it…

Using the Eclat algorithm in Python to do frequent itemset mining for basket analysis — In this article, you will learn everything that you need to know about the Eclat algorithm. Eclat stands for Equivalence Class Clustering and Bottom-Up Lattice Traversal and it is an algorithm for association rule mining (which also regroups frequent itemset mining). Association rule mining and frequent itemset mining are easiest…

Using the FP Growth algorithm in Python to do frequent itemset mining for basket analysis — In this article, you will discover the FP Growth algorithm. It is one of the state-of-the-art algorithms for frequent itemset mining (also called Association Rule Mining) and basket analysis. Frequent Itemset Mining and Basket Analysis Let’s start with an introduction to Frequent Itemset Mining and Basket Analysis.

Using the famous Apriori algorithm in Python to do frequent itemset mining for basket analysis — In this article, you’ll learn everything you need to know about the Apriori algorithm. The Apriori algorithm can be considered the foundational algorithm in basket analysis. Basket analysis is the study of a client’s basket while shopping. The goal is to find combinations of products that are often bought together…

All you need to know about the F1 score in machine learning. With an example applying the F1 score in Python. — Introducing the F1 score In this article, you will discover the F1 score. The F1 score is a machine learning metric that can be used in classification models. Although there exist many metrics for classification models, throughout this article you will discover how the F1 score is calculated and when there is added value…