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The future of chatbots?
Natural language processing

The future of chatbots?

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?

Predicting employee turnover
Ai for business

Predicting employee turnover

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.

Automated ML: the end of the data scientist?
Ai ethics

Automated ML: the end of the data scientist?

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.

How does project management work in data science?
Data science project management

How does project management work in data science?

Why is it so difficult to apply traditional project management to data science projects? How to make your data science project management go smoothly.

Starting a data science project
Data science project management

Starting a data science project

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.

Matchmaking with deep learning: recommender systems for dating
Natural language processing

Matchmaking with deep learning: recommender systems for dating

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.

How can we eliminate bias from AI algorithms? The pen-testing manifesto
Ai ethics

How can we eliminate bias from AI algorithms? The pen-testing manifesto

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).

How to predict how much a group of customers will spend
Ai for business

How to predict how much a group of customers will spend

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.

How well can you predict an individual customer's spending habits?
Ai for business

How well can you predict an individual customer's spending habits?

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.

How to improve conversions without losing customer data
Ai for business

How to improve conversions without losing customer data

Long forms reduce conversion rates You may have had the experience of filling out a long form on a website. For example, creating an account to make a purchase, or applying for a job, or renewing your car insurance.

Building a face recogniser: traditional methods vs deep learning
Ai for business

Building a face recogniser: traditional methods vs deep learning

Why is face recognition everywhere? Face recognition technology has existed for quite some time, but until recently face analysis was not accurate enough for most purposes.

How forensic stylometry can identify the author of a document
Natural language processing

How forensic stylometry can identify the author of a document

Click here to see a live online demo of the neural network forensic stylometry model described in this article. In 2013 JK Rowling, the author of the Harry Potter series, published a new detective novel under the pen name Robert Galbraith.

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