Although academic research in the area of machine learning dates back to the 1950s, it's not up until recently we've seen major breakthroughs such as advanced natural language processing, self-driving vehicles and robotics.

As machine learning becomes more mainstream, it's also becoming more and more accessible to software engineers. We're in the middle of the democratization of AI. There are numerous ways to get started, ranging from variety of open-source libraries to specific offerings by cloud providers (e.g. AWS and Azure). It's easy to get lost.

Join me in this session in which we'll cut through the machine learning jargon, and explore what options are available to you, so you can get started today!