Handling frequency in machine learning One of the trickiest situations in machine learning is when you have to deal with datasets coming from different time scales. Let’s say, for example, that you are handling financial data, and some of the data comes at a monthly frequency (e.g. sales reports), and Read more…
Solving machine learning problems Solving a machine learning problem can be a daunting affair for beginner data scientists. There are simply so many algorithms to choose from! Simply go to scikit-learn‘s page and you are already overwhelmed by all the options! One of the main challenges is that if you Read more…
Artificial Intelligence is eating the world Technological advancement and digital innovation in the last few years have been instrumental in defining the success of businesses. Artificial Intelligence (AI) has made a huge impact in the world of business in the last decade, as one of the best ways of making Read more…
Problems in statistics Something which often frustrates me is seeing how many statistical models are wrong. I am referring to scientific journals, but also commercial models. Something which you’re going to ask me is what I mean “wrong”. Some might argue that models can’t be wrong or right. They simply Read more…
Getting started with anomaly detection Anomaly detection is one of the most interesting applications in machine learning. While anomaly detection can be done in a both supervised and unsupervised manner, in most cases, it is done through unsupervised algorithms. One of the best ways to get started with anomaly detection Read more…
Gradient boosted trees Gradient boosted trees is one of the most popular techniques in machine learning and for a good reason. It is one of the most powerful algorithms in existence, works fast and can give very good solutions. This is one of the reasons why there are many libraries Read more…
When you stat learning about data science, it is important to be organised. Many data scientists tend to be messy by nature. Tools like Jupyter notebooks don’t really help much. While they can be very versatile and useful for experimentation, they can unfortunately make you learn the wrong habits. Some Read more…
Parameter optimisation is a tough and time consuming problem in machine learning. The right parameters can make or break your model. There are three different ways to optimise parameters: 1) Grid search. 2) Random search 3) Bayesian parameter optimisation Grid search Grid search is by far the most primitive parameter Read more…
Cohen’s Kappa statistic is a very useful, but under-utilised, metric. Sometimes in machine learning we are faced with a multi-class classification problem. In those cases, measures such as the accuracy, or precision/recall do not provide the complete picture of the performance of our classifier. In some other cases we might Read more…
AI and machine learning are an excellent way to help SMEs achieve their goal of consistent business growth in today’s competitive environment. They allow SMEs to effectively delegate routine tasks to be done by innovative yet fast methods/technologies making executives free to focus on other creative avenues that can help Read more…
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