Machine Learning 1. Where to start?

On being a Machine Learning Engineer

The hype around ML/AI is dense. It’s undeniable that machine learning has seen some amazing progress in the past few years! It’s also a truism to say that  or  are the sexiest job titles to have right now. But there are a few problems about ML that hardly people bring up. This talk will address the unglamorous truths about ML and AI as seen form the eyes of a practitioner and educator in these domains.

Linear Regression from scratch

In this talk we will cover the staple of machine learning, the linear regression. We'll slowly walk you through a raw implementation and discuss it in detail, complete with gradient descent and visualisations.

New in ML. What should I choose?

Every new start comes with choices. And often, these (uninformed) choices impact the result you'll ultimately get. If you've made the right choices in the beginning, the learning should be meaningful and enduring. On the other hand, if your choice was somehow unlucky, you may end up frustrated and give up at some point. Imagine having to choose between learning Perl or Python in the 2000, or between programming in NetBeans, Eclipse of IntelijIDEA in 2008, or between developing for Symbian or iOS in 2009, or learning CoffeScript vs TypeScript in 2014 etc..

At some point, all these options look equally valid, but in the end, experience in only one matters.

In this talk we'll make a survey of what are your options when trying to start learning ML. We will look at programming languages, IDE's and tools, libraries you need to master, and ML frameworks. At the end of this talk you should have a clear grasp of what lies ahead, and what to choose from. I'll also share my view of what to use, if you're just starting out.