In an ever-more competitive world, business-intelligence is vital. Data science, advanced analytics, and machine learning enable organizations to make smarter, qualified, data-driven business decisions.
And that’s why we now host Rafal Lukawiecki’s Practical Machine Learning courses in the UK.
Why are we offering these courses?
Oxford Computer Training has specialized in identity and access management, and security. So what has this to do with machine learning? Well, potentially, quite a lot – but while there is great interest, there are practical obstacles that need to be overcome. Notably, the quality and completeness of available data. However, we believe that the area is ripe for practical applications, and that now is a good time for identity practitioners to get skilled up.
History and association
Our CEO and Founder, Hugh Simpson-Wells, and Rafal Lukawiecki of Project Botticelli became colleagues in 1990 and have been friends ever since.
Hugh says: “I have followed Rafal’s progress as he has carved out a niche this particular area of expertise. He has incredibly high standards, and I know this is going to be a great course – plus I am excited to be working with Rafal again!”
Rafal says: “Because of Oxford Computer Training’s obsession with important detail, and their unrelenting focus on the learning outcomes for the student, I cannot think of a better training company to host my courses in the UK.”
What will you learn on Rafal’s machine learning courses and who are they for?
The courses are for analysts, budding and current data scientists, database and BI developers, programmers, power users, DBAs, predictive modellers, forecasters, consultants, ML/AI engineers – and anyone interested in using ML for AI. You will learn the essentials of starting data science, ML, and AI projects and the fundamental concepts. Rafal will show you how to avoid common pitfalls, and how to work fast yet accurately. He will teach you what is really useful and practical, what is more theoretical but still important, and finally, what hype you should be wary of.