Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.
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.
During the last year, we’ve experienced the escalation of social issues around artificial intelligence (AI), with Elon Musk leading the charge. Musk continues to advocate the idea that humanity is getting closer to a Skynet-like future – to many people’s concern. One of the very real and valid concerns is the idea that many existing jobs will be automated, thanks to AI.
Data mining is another term that is often confused with machine learning (ML). Here’s an easy explanation of the two terms, as well as the relationship between the two.
With all the possibilities it presents, machine learning is on many a company’s to-do list for 2018. You could determine the optimal time and number to reach prospects and debtors on, improve cross-sell and upsell rates by offering the perfect next product at the perfect time or you can know when to give which customer a discount or special offer to prevent churn. But the costs of implementation of a machine learning tool in just one business area is high.
Machine learning has the power to transform the world, and it’s not just being used to power chatbots or to recommend your Netflix content. There are many amazing machine learning applications that improves our everyday live, but also makes the world a magical place.