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…
Metrics in machine learning As some of you might know, metrics in machine learning is one of my favourite topics. I have already spoken about RMSE and MAE, Cohen’s Kappa, the concordance correlation coefficient and other metrics. On this post, I’d like to talk about another very important metric: The Read more…
Note: This is an excerpt from my new book-in-progress called “Uncertainty”. The most popular definition of probability, and maybe the most intuitive, is the frequentist one. According to frequentists, an event’s probability is defined as the limit of this event’s frequency in a large number of trials. What does this Read more…
AI and creativity As many of you know, I am a big fan of the space of AI and creativity. This is a space I have worked on and blogged about in the past, and I am still active in. I am also the admin of the AI and Creativity Read more…
Artificial intelligence (AI) is not only an emerging technology but it is also highly complex. There isn’t even a settled definition of AI, not least because AI is a catch-all term that encompasses various types of technology. This provides plenty of opportunity to theorise as to what the future will Read more…
Data science is ever evolving, and with so many things going on it can be difficult to keep track of all the new libraries and algorithms. This is when it can be really useful to have a reference guide to help you out. Continuing from our series about cheatsheets, in Read more…