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.
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. In fact, many data scientists and non-data scientists might expect this kind of idealised data science project flow.
What is unsupervised learning? When we think about acquiring a skill or learning a new subject, most of us see that process involving a teacher passing their knowledge on to us. If you’re teaching a child how to distinguish between different fruits for example, you might show them various images, identifying one as an apple, another as a pear and so on, so that when the child sees these fruits in real life, they can recognise which is which themselves, but initially via the labels you provided. This is known as supervised learning, and is one way in which Artificial Intelligence uses Machine Learning to predict particular outputs, having used data points with known outcomes. However, this is not the only way we, or computers for that matter, learn. Let us introduce to you Unsupervised Learning.
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