Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.
We’ve compiled this list of 10 people you should take note of in the machine learning field to keep yourself updated and informed about the field. It's also useful if you're interested in learning about machine learning, as these are the people who not only influence the industry but are paving the way forward and shaping the future of machine learning. We've given a short overview and a video to familiarise yourself with each of them, but if you're invested in learning about machine learning, follow them on social and start reading their publications.
Machine learning and artificial intelligence are exciting fields, and we've been writing about these topics for a couple of years now. While a lot of what we talk about on our blog is advanced implementations of machine learning and can be overwhelming to beginners, the core concepts of machine learning are actually pretty easy to grasp. There are many resources and cheat sheets available online, but we believe the old fashioned way of learning is sometimes the best: with a good book. Few resources can match the in-depth, comprehensive detail of a good book.
Our data scientists are keen readers and avid podcast listeners. In this blog, we list a few of the podcasts that cover topics such as data science, machine learning and artificial intelligence, and that we’d recommend if you’re looking to start exploring the world of podcasts.
We are quite proud of the ability to develop performant, stable and trustworthy predictive models here at Principa. For nearly 20 years, we have been developing predictive models that have helped so many of our clients to make better decisions, more often than not outperforming what our best competitors can achieve. The models that we have historically developed can be categorised as part of the additive group of models – that is, a handful of predictive characteristics are selected and classed in a way that best separates the ‘goods’ from the ‘bads' (i.e. the traditional binary classification application). Depending on the new unseen data, the resulting weightings are then added together to get a final score. For example, consider a 3-feature model that uses only Home Ownership, Years at Employer and Age. Let's say you are a homeowner and for this you get 10 points, you have been with your employer for 5+ years (15 points), and you are 23 years of age (8 points), then your final score is 33, and the strategy will use this score and decide where you should go in the business decision tree.
Machine learning models can be used very successfully in many different contexts to predict outcomes for different use cases accurately. These predictions can be used within the business to make better decisions or to operate more efficiently (or both) and can give you an edge over your competitors. Predictive models all follow the same recipe – i.e. train a model on historical data and then apply this model to unseen data to get predictions. If your model generalises well, you have a prediction that you can trust and use to decide "do this, not that" with some degree of accuracy.