Many applications of machine learning in business are complex, but we can achieve a lot by scoring risk on an additive scale from 0 to 10. This is a middle way between using complex black box models such as neural networks, and traditional human intuition.
Natural language processing (NLP) is the science of getting computers to talk, or interact with humans in human language. Examples of natural language processing include speech recognition, spell check, autocomplete, chatbots, and search engines.
There are many differences between deep neural networks and the human brain, although neural networks were biologically inspired. When was the last time you looked at a picture of an animal, bird or plant, couldn’t place it straight away and were left flabbergasted? When we look at something, our brain fires off a series of neurons that runs that image across thousands to millions of ‘reference images’ stored in the brain. In the case of the unusual animal or bird that we spotted: size, diet, habitat, lifespan, and so on.
What is sentiment analysis and what are the key trends in sentiment analysis today? Understand and try out some of the simplest and most cutting-edge sentiment analysis technologies!
Natural language processing can distinguish customers from salespeople Is it possible to use natural language processing (NLP) to distinguish between unwanted sales approaches and promising leads for a business’s customer relationship management? If so, this would be a great application of AI in business.
Today, Thursday 5 May, is being celebrated worldwide as the International Day of the Midwife, organised by the International Confederation of Midwives.
Job postings for data science consultants have increased an amazing 256% since 2013. Why? The need for data collection and processing is everywhere. Nearly all businesses – from large corporations to local companies – need someone to manage and interpret their data. There are also more businesses today that use artificial intelligence, and machine learning to improve or automate tasks like customer personalisation, recommendation engines, churn prediction, cost modelling, and other key business functions. There is also a growing demand for niche areas of data science such as natural language processing or computer vision to enable industries such as insurance, healthcare, legal and pharma to process huge quantities of data in text or image form.
Some ways that we can model causal effects using machine learning, statistics and econometrics, from a sixth-century religious text to the causal machine learning of 2021 including causal natural language processing.
Named entity recognition (NER) is the task of recognising proper names and words from a special class in a document, such as product names, locations, people, or diseases. This can be compared to the related task of named entity linking, where the products are linked to a unique ID.
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