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Hacking Machine Learning: A hands-on workshop for developers
Machine learning and data science are among the fastest growing fields in the tech world. According to Linked in, software engineers with machine learning and data science skills are the most sought-after, and the highest paid among their peers.
While developers are used to learning new tools, languages, and frameworks on a daily basis, learning machine learning is a different beast: while one can still apply certain programming and abstract thinking skills to learning machine learning, she also needs a decent understanding of the fundamental mathematics behind it: statistics, linear algebra, optimization, etc.
In this workshop, we will try a different approach for teaching machine learning to developers: like learning any other new library, we learn how to use machine learning tools and packages and apply them to interesting, data-oriented, problems. But we wouldn’t stop there: we learn fundamental differences between some of the most common, and most useful, tools in machine learning (such as SVMs and Random Forests). We talk about fundamental concepts, such as classification, clustering, and regression, and learn how to apply them to practical, real-world problems: for example, how to use an SVM for analyzing Donald Trump's tweets.
But more importantly, we learn how to think about machine learning and data science, and how to learn new concepts. There are fundamental differences between learning machine learning and, say, a new library or framework, and this workshop tries to clarify and these differences, and prepare you to learn machine learning on your own.
Ali will review the mathematical concepts required for each part of the workshop (vectors, matrices, trees, linear equations, ...), but having a basic understanding of high-level mathematics is recommended.
Ali Alavi (https://www.linkedin.com/in/alialavia/) started his career a software engineer and since has worked on a wide range of topics, including embedded systems, full stack development, and autonomous vehicles. He has done graduate research work on Natural User Interfaces, on how to turn in-air human gestures into computer input. During this work, he had to learn a variety of new topics, including machine learning and data science, and apply them to the problem at hand. He tries to use this experience as a vessel to teach software engineers (his peers) to quickly learn the tricks of the craft.
This workshop is presented by Devhub, Canada's only co-working and community space for software developers and programmers. In this new series of workshops, Devhub is stepping into the educational space with a set of events aimed at expanding the skill sets of developers, both experienced and beginner. Check out our other events here.