How to Become an AI Specialist

By IU International University of Applied Sciences Modified on January 26, 2023
Tags : Academics | Careers | STEM

Ever wonder how AI works and how you can find a career in it?

 How to Become an AI Specialist

Ever wondered how AI works and how you can start your career in AI? If you’re reading this, you’ve probably been asking: “How do I become an artificial intelligence specialist?” So, let’s start at the beginning.

What is artificial intelligence?

Artificial intelligence allows computers to mimic human intelligence with abilities in decision-making, reasoning, and perception. And this newly developed capability to learn, plan, and solve problems means computers can now quickly solve human tasks, thus freeing up more of our time.

What is artificial intelligence used for today? Well, in recent years, artificial intelligence technology has become present almost everywhere in our tech products. Think search engine query suggestions, image recognition, and chatbots — you’ve even got smart compose for emails that suggests full phrases. And with the massive amounts of data being collected all over the world, AI is getting smarter, more capable, and more widely adopted. In fact, the AI market worldwide was valued at €91 billion last year. With new developments in autonomous transportation, medicine, and even entertainment, the need for AI specialists is skyrocketing.

What do AI specialists do?

The science of AI is made up of three major fields of expertise that deal with everything from machine learning to robotics. Yet there is one constant that links every artificial intelligence career path: data. Explore the three fields of expertise in AI:

AI data scientist — AI data scientists build theoretical learning models in a programming environment which prepare a neural network to solve real-life problems. They use analytical methods to identify trends and patterns for the AI to correlate within the data they feed it.

Machine learning engineer — Machine learning engineers focus on creating and managing AI systems and predictive models.

Big data engineer — Big data engineers are responsible for interpreting and correlating large sets of statistical data. They also analyze forecasting data and build predictive models for future information gathering and interpretation.

How to start your career in AI

If your goal is to figure out how to work in artificial intelligence, there are a few steps you should be aware of before setting off:

Step one: The fundamentals, from basic to complex

If your background involved statistics and computer science, you’ll have a head start. If not, you’ll need to brush up on your matrices, linear algebra, and calculus. Knowledge of statistics and probability is a must because you’ll be working with relational and non-relational databases. And you’re only getting started.

You’ll also have to learn the basics of Python: expressions, variables, data structures, functions, and packages such as pip. Next, you’ll need to learn some important data-handling libraries such as pandas, NumPy, and Matplotlib. And then you’ll have to get your hands dirty with Virtual Environments.

Step two: Hit the books

Before you figure out which of the three major AI career paths tickles your interest, you’ll first have to learn how to process data. This is the biggest step in figuring out your path and it involves:

  • Principal component analysis
  • Dimensionality reduction
  • Normalization
  • Data scrubbing and handling missing values
  • Unbiased estimators
  • Features extraction
  • Denoising and sampling

Save yourself a lot of time and get a recognized university degree. At IU International University of Applied Science, we offer both a Bachelor’s degree and a Master’s degree in artificial intelligence which you can study completely online or on campus. A degree shows potential employers that you’ve got a firm grasp of the basics and that you’re ready to start. Getting certified could also fast-track your career because all the practical experience you get results in projects you can create a portfolio with.

Step three: Gain experience

They say experience is the best teacher. What they don’t tell you is that the most data-rich point in any scientific endeavour is failure. And that’s why you should always be as hands-on as possible. When it comes to science or learning, failure is a primordial step that cannot and should not be avoided.

But you can’t fail if you don’t try stuff. You can’t see what works and what doesn’t if you don’t play around with the programming yourself. So, don’t wait until you graduate to start working on your own projects. It’s only by trying to implement what you learn that you figure out where you went wrong. Failure can be frustrating, but if you shift your perspective, failure — and the process of learning — can be one of the most rewarding parts of your day.

Step four: Network

While demand for AI specialists far exceeds the number of people in the field, your success necessitates some creativity. Sure, your degree and project portfolio will do a lot of the talking, you’ll still have to walk the walk yourself. And to be noticed, you’ll first need to build your crowd.

If you’re like most people, you probably don’t have a lot of AI specialists in your circles. And this is where online platforms come in handy. You can search for people who work in AI on job sites like LinkedIn. Don't feel awkward about just adding them to your network out of the blue — people with similar interests tend to support each other. And don't forget that your reach is global.

From here on out it's all about being active in your community — make posts about your questions on AI, comment on other people's posts, and don't forget to smash that like button! Success is always built with people. You'll learn a lot faster too if you put yourself out there and ask for feedback on your projects. You never know, you might just impress the right person and land an internship!

Benefits of a career in AI

So, is a career in AI worth it? Here’s a quick summary of why you should be looking forward to making this decision and sticking with it:

It’s future proof — This is the bleeding edge of computer science. There’s no denying that intelligent machines are changing everything and creating countless job opportunities worldwide.

Great pay — because AI is seeing eye-watering adoption speed in pretty much every industry sector, salaries are pretty impressive too.

Never stop learning — Your work is as mundane or as exciting as you want to make it. There’s no limit to AI application, as it’s constantly evolving and expanding.

Now you know what a career in AI looks like, you know the artificial intelligence career paths, and you understand how AI works. You’ve got a roadmap and know how you can start your career in artificial intelligence. Excited?

Start your AI education at IU today

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