Unstructured Data and Management – Overview To explain it in very generic terms, unstructured data is information which is not organised or immediately interpretable. It’s often text-based, although it can include images, numbers, dates, and other details which can be useful to a business, and which can be valuable for AI initiatives in the business.
Guest post by Essa Jabang, who works as a data and engineering consultant in our team at Fast Data Science and also runs his own company Taybull.
Text Mining: Quick overview The modern enterprise has access to vast amounts of unstructured data but that data can only prove useful if the desired insights can be extracted from it.
Read more about AI for business on fastdatascience.com
What are artificial neural networks and how do they learn? What do we use them for? What are some examples of artificial neural networks? How do we use neural networks?
How Hybrid AI can combine the best of symbolic AI and machine learning to predict salaries, clinical trial risk and costs, and enhance chatbots.
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 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.
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.
What we can do for you
