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.
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.
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.
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.
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?
Register for the launch event on 29 March 2021 here (registration has passed). Click here to visit the dashboard. Visit our What Women Want dashboard!
Is AI going to be a jobs killer? How many jobs will be lost to AI? What jobs will AI create?
What are the key stages in a data science project? A recipe for a successful data science project flow. The naive view of a data science project’s stages When planning a data science project, it’s easy to think of it as a simple exercise in a little bit of data cleaning, some data science work, and a deployment stage at the end of the project.
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