Analytics, Machine Learning

Tips For Getting Off The Ground With Machine Learning

Across the last decade, people in industries of all types and varieties have discovered the amazing power and potential of data. Data is commonly referred to as the new oil, a powerful resource that is only now being tapped into for immense results. All sorts of different things have become possible through data, things that will have a huge influence over the shape of the decade to come. Using data to learn about the world is one of its primary uses, a use that is valuable to all sorts of different professions; from early cancer diagnoses to online shopping trends, we now know more about the world than ever before, because of data and using it with greater care. One use of data that has the potential to reshape the way we operate in all fields is through machine learning.

Machine learning uses artificial intelligence to help machines (computers) to teach themselves about something. Once they have a certain volume of data they can piece together the rest in a way that doesn’t require human input. The power of this could be immense, with machines being able to understand the world on a level comparable to how a human understands it (though through a different route). Machine learning is and will be an important industry to get into. But it’s a very hard one to break into, especially as someone completely new to the field. So here are some tips to help you break-in.

Mathematics Is Key

Mathematics is key. Robot showing math on blackboard.

This is unfortunate for anyone who isn’t a fan of math, but it’s an unavoidable part of machine learning. “Machine learning involves lots of data, lots of numbers and the use of algorithms. It seems obvious to point out but, in some way or another, that means everything has to do with math.

You have to have high-level calculus experience to get involved in machine learning, at least at the ground level”, says Jason Ming, machine learning engineer at DraftBeyond and ResearchPapersUK.

You can’t skimp on this really. There are other areas that you can get involved in higher up, like working for companies who are looking to implement machine learning, but generally, rigorous education in mathematics is important.

Keep Your Ear To The Industry

Machine learning is one of a handful of tech and data industries that are moving very fast. Technology has traditionally been a fast area and taking a year out from it can feel like the equivalent of a decade in other industries. Machine learning will make advancements in the coming year or two that will change the ‘meta’ in a way that will impact everyone working in it. This means that, even if you haven’t yet found work in the industry, you need to have a watchful eye on the industry to ensure that you aren’t left behind. Go to industry events, be on blogs and forums, and find contacts in the industry who will keep you up to date on the future of the industry. You need to keep in step with the changes to make it most likely that you can break-in.

Programming Will Help

Computer programming helps to get a machine learning job.

There’s important programming that needs to be done to get machine learning started. This means that as well as mathematics it would be very sensible for you to have strong skills in C++ and knowledge of coding algorithms. Combine this with some understanding of business and tech for business and you should have a great platform from which to launch your career in machine learning.

Get A Project Done

“It’s very hard to find a position in machine learning without first having evidence of some sort of project under your belt. If you can get something done whilst in college that would be ideal, but it’s possible to get stuff done on your own as well”, says Laura Mackey, a programmer at LastMinuteWriting and Writinity.

Get the basics of ML sorted through online tutorials and do your own project. No matter how small it is, it shows initiative and baseline experience, which is key for employers.

Conclusion

As you can probably tell, breaking into the machine learning industry is not easy. It’s a bit of the classic, ‘you need the experience to get experience’ chestnut. But if you follow this advice, you should be set up nicely to seize the first opportunity which comes.

Harry Conley works as an editor at LuckyAssignments and GumEssays, with a particular focus on tech and business, always looking to develop writers with useful instruction.

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