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Machine Learning - Frequently Asked Questions
What is machine learning is used for?
Machine learning is instrumental in the processing and predicting power of data, utilising AI technology to learn and adapt without manual input.
Essentially, it gives computers the power to process and learn from data without programming. There are a number of real life applications of machine learning, including image recognition, spam filters, statistical analysis and data extraction, to name a few.
How do business use machine learning?
Businesses are increasingly harnessing the power of machine learning to deliver better processes and insights. Machine learning can take manual tasks away from data professionals, providing more time and capacity to focus on other areas that do require expertise and manual inputs.
Machine learning is also used in a number of business applications, such as website chat bots, market research and data predictions and forecasting. And as AI gets more powerful in future, so too will the power of machine learning.
Which roles need to understand machine learning?
Machine learning is most beneficial to those in the IT and data areas of an organisation, including those who work in data science and data analytics. Emerging job titles such as AI data analyst or AI scientist should also be highly familiar with machine learning.
How to learn machine learning
There are several courses that can lead to career in machine learning, including Practical Machine Learning and the Fundamentals of Machine Learning.
Undertaking a Data Apprenticeship can also be a viable route into becoming a machine learning specialist, with opportunities for training and to become certified in data science and machine learning.
What does a machine learning engineer do?
A machine learning engineer (ML engineer) is responsible for building systems capable of utilising artificial intelligence and machine learning. They are likely to spend their day to day working on AI algorithms and experimenting with different datasets and approaches, which are then used to drive tangible business insights.
Their skills set may include experience in statistics, data and programming languages, such as Python, R or SQL.