Machine Learning is just for people with PhDs or mathematical backgrounds, right? Where would I even start with teaching machines to learn?
Some Liberty IT engineers got together in the form of a Virtual Tech Talk, to chat about these questions and more - with a wide range of employees joining the call to watch a panel discussion and take part in a Q&A session.
Vicky Moulds, who chaired the event, has put together a summary of some of the key topics that were discussed, and some additional information on how you can get started with Machine Learning.
Vicky's Medium blog post brings together contributions and recommendations from the four panel members - Mairead O'Cuinn, Darren Broderick, Nikita Armstrong and Gillian Armstrong.
Artificial Intelligence and Machine Learning already plays a big part in our lives: filtering spam emails, giving music and movie recommendations, showing you targeted advertising, helpful chatbots and digital assistants. The use of AI and ML is set to grow with a Gartner study from 2019 showing that leading organizations expect to double their number of AI projects in 2020 and have even more in place by 2022. As engineers we should be making sure that we have the right skills to be able to leverage AI and ML services as appropriate to help stay ahead and be competitive.
Vicky goes on to discuss the direction of ML, whether you need a PhD to do ML (spoiler... the answer is no!) and ethics in ML, and provides a really useful list of resources for those getting started, even if you're a complete beginner.
One great example is DataCamp — where you'll find great learning materials for beginners into ML that are looking to get familiar with Python and R basics as well as more advanced courses.