How we explain how a neural network can recognise an image? Sometimes as data scientists we will encounter cases where we need to build a machine learning model that should not be a black box, but which should make transparent decisions that humans and businesses can understand.
More than 80% of data science projects fail and never deliver an ROI for the business. What’s behind the high failure rate and how can we change this?
Left: a benign mammogram, right: a mammogram showing a cancerous tumour. Source: National Cancer Institute You may have read about the recent Google Health study where the researchers trained and evaluated an AI model to detect breast cancer in mammograms.
You may have seen the news about Facebook’s new futuristic chatbot trained for empathy on 1.5 billion Reddit posts. You might be wondering, how it is possible to make a computer program converse with humans in a natural way?
One challenge that large organisations face today is the problem of understanding and predicting which employees are going to leave the business, called employee turnover prediction or workforce attrition prediction.
What is automated ML? Automated machine learning is software which in theory allows anybody to design, train, and deploy machine learning models to production environments without needing to write any code.
Why is it so difficult to apply traditional project management to data science projects? How to make your data science project management go smoothly.
It is often quite complex and time-consuming to get a data science project off the ground. So I am sharing some of my thoughts and my checklist for what needs to be in place to get a data science project started.
Recommender systems in retail If you’ve ever bought something on Amazon or other large online retailers, you’ll have noticed the ‘similar products’ that the site recommends to you after you’ve made your purchase.
Gender bias in credit scoring AI? In recent weeks a number of Apple Card users in the US have been reporting that they and their partners have been allocated vastly different credit limits on the branded credit card, despite having the same income and credit score (see BBC article).
Earlier I wrote another post about predicting the spend of a single known customer. There is a related problem which is predicting the total spend of all your customers, or a sizeable segment of them.
Why do we need to predict customer spend? You may have read my previous post about customer churn prediction. Another similar problem that’s just as important as predicting lost customers, is predicting customers' daily expenditure.
What we can do for you
