How to build a career in AI and Machine Learning
If you are considering building a career in machine learning (ML) and Artificial Intelligence (AI) then you need to have something of a disciplined approach. If you are currently a professional in the field of IT then you might be looking at a change of lanes due to the vast opportunities that this new industry has. Alternatively, you could just be someone who is curious about AI and ML and has been looking for the right time to pursue a career in it. Whatever your motivation is, you need to have a strategy to ensure a smooth transition from your current career path, to one in ML and AI.
What do you already know?
Like any industry, AI and ML come with their own set of requirements and qualifications that you need to master in order to be successful. You cannot just decide one day to move into this sector, and to get a job straight away. The learning curve is a steep one and requires you to have a set of basic skills that will lay the foundations for your journey. As someone looking to move into AI you need to have a basic knowledge of the following:
- Probability and statistics
- Coding and algorithms
- Applied mathematics
- Programming languages including R, Java and C++
- Distributed computing
Once you are sure you have a basic understanding of these, you can start building on these skills and others to help you become the top of your game. Get books on statistics, take courses on programming, get first hand experience of coding, work on your weaknesses, improve further with your strengths- just continue to build on what you already know, and learn what you don’t. Whilst you do this it is also advisable to sign up to a course on artificial intelligence and machine learning so you can get to grips with the basics as well as get a handle on more advanced concepts.
While self-learning is great, you will need some mentoring to ensure that you stay on track and don’t lose your motivation. See if you can sign up for a course that you have to attend in person, meaning you are accountable to real life teachers and have the opportunity to network with like minded individuals. In addition to this, more complex learning that includes complex concepts and algorithms need to be taught by a faculty or expert who can answer questions and advise you as you go.
Hands on experience
It is no good knowing all there is to know about AI and ML, but not knowing how to use it in practice. Working on real-life projects gives you the chance to garner ample experience in the field as well as to boost your CV. When you apply for a job, what real experience you have had putting your skills into practice is what potential employers are going to look at, more than what grades you got in your exams. Collaboration, growth, innovation and a hands-on approach to learning is what every good employer is looking for.
Now you have a good understanding of what is required to build a career in AI and ML, it is time for you to start putting the wheels in motion. Figure out your strengths and weaknesses, enrol in a course, and lay the foundations for an exciting and rewarding career in one of the most dynamic industries out there.
Authored by the Finerton.com News Team (Malta)
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