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.
Data science in an organisation starts with three separate sub-teams: the data science team, the data engineering team, and the data operations team.
Does protecting sensitive data mean that you also need to compromise the performance of your machine learning model? If you study machine learning in university, or take an online course, you will normally work with a set of publicly available datasets such as the Titanic Dataset, Fisher’s Iris Flower Dataset, or the Labelled Faces in the Wild Dataset. For example, you may train a face recognition model on a set of celebrity faces that are already in the public domain, rather than private or sensitive data such as CCTV images. These public datasets have often been around a very long time and serve as useful benchmarks that everyone agrees on.
How NLP document similarity algorithms can be used to find similar documents and build document recommendation systems. Let us imagine that you are reading a document, and would like to find which other documents in a database are most similar to it. This is called the document similarity problem, or the semantic similarity problem.
How can natural language processing and data science help non-profits (charities) such as the White Ribbon Alliance analyse survey data and share it with the world? We have developed an interactive dashboard allowing members of the public to explore women’s healthcare requests around this world. We believe this is a first for transparency in the third sector. Click here to view the dashboard that Fast Data Science developed for the White Ribbon Alliance.
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