In this article I consider the task of clustering a collection of vectors into groups or clusters of vectors that are close to each other, as measured by the distance between pairs of them. In particular I focus on the famous clustering method, called the k-means algorithm, and give some typical applications.
To understand even the simplest machine learning algorithm requires a solid grasp of the basics of linear algebra. We can think of linear algebra as a foundational tool that provides the mathematical framework necessary for when we later build and interpret sophisticated machine learning models. In this article, I'll guide you through the core concepts of linear algebra, starting from simple scalar arithmetic and progressing to the complexities of matrix multiplication.
Topic modelling is a form of text analysis that uses unsupervised machine learning to identify patterns, themes, clusters, and groups across a collection of documents. In this article I discuss using the powerful BERTopic library alongside quantized large language models to identify themes and topics from a collection of research papers at the intersection of artificial intelligence and ophthalmology. Then, we'll use the DataMapPlot library to produce a publication ready visualization of the thematic structure contained within the abstracts of those research papers.
Google Colab (or Colaboratory) is a complete, modern, cloud-based runtime environment. It gives individuals and teams the ability to work together on coding, data science and machine learning problems with shared access to data, state of the art GPU's and TPU's, and industry standard Python libraries. You are provided with an executable document, i.e. a notebook much like a Jupyter Notebook, that allows both code and markdown to be written and executed, and your results visualized. In this article I'll cover one of the most fundamental requirements all teams will face - how to get data into your Colab notebook in the first place!
Financial institutions (including banks, insurance brokers, investment companies, etc.) are obliged to guarantee the highest level of security and protection against (an increasing) range of methods criminals use to commit financial crime. The UK Payment Services Regulations, for debit cards, state that the most you should have to pay is the first £35 of an unauthorised transaction if the bank has reason to believe you should've been aware that your payment details were lost or stolen. Your bank can only refuse to refund you if it has evidence that you acted fraudulently, or with 'gross negligence'. If a fraud occurs on your credit card or a credit facility, the UK Consumer Credit Act takes precedence. Thus, any accusation of 'gross negligence' on the part of the consumer doesn't arise in this act unless your card provider can demonstrate that you authorised the payment. Consequently it stands to reason that banks and financial institutions are especially interested in preventing bank fraud! In this article I'll look at the role machine learning can play in preventing credit card frauds - while also considering some of the specific challenges this domain presents.