Machine learning (ML) is a promising area of artificial intelligence development, allowing you to create algorithms and models that can learn from data and make decisions. We will tell you how work in this area works and what you need to develop in ML.
Specialties in ML
In the ML world, there are two main roles: Research Scientist and Research Engineer.
- A Research Scientist forms the scientific agenda and determines the directions of future research, develops mathematical models, and conducts theoretical research.
- A Research Engineer is focused on the practical implementation of these models: he writes code and tests hypotheses.
Required Skills for a Researcher
Where to start? What skills will be needed?
- Mathematics is the base. It helps to understand complex concepts and algorithms, so without a good understanding of linear algebra, statistics, and probability theory, it will be difficult to move towards ML.
- Programming. The main programming language in ML right now is Python, it's easy to learn and provides a bunch of libraries, making it easier to work on ML projects. But it is important not only to learn how to write code, but also to understand how to test hypotheses and build a logical chain of research.
- English is the international language of science. For reading and writing articles, communicating at conferences, and collaborating with colleagues from all over the world, English is a must.
- You will write scientific articles and defend your ideas in front of reviewers, which will require argumentation skills and a clear presentation of your thoughts.
- Initiative – it is important to take the initiative in research work. No one will tell you exactly what to do! You will be looking for interesting tasks and ways to solve them yourself.
- Even if you're behind a project alone, at some point you'll have to interact with colleagues and share your findings, so it's important to be able to work in a team. Collaboration allows you to share knowledge and find better solutions.
- Patience. Scientific research is a path full of unknowns. You must be prepared for the fact that your hypotheses will sometimes be wrong, and the road to the result will be long and difficult.
Science in industry and academic science
Research in ML is carried out on the basis of universities and large companies.
- Universities usually offer a freer environment for basic research. Here, scientists do what they are really interested in, but the disadvantage may be limited resources and access to big data.
- Large companies such as Google, Facebook, and Microsoft have huge resources and access to great amounts of data, and a close connection with the business allows you to quickly find relevant and practical tasks. However, here the freedom of scientific research may be limited by the business goals of the company.
Career path to ML
The process of becoming an ML researcher usually takes from 4 to 10 years, so the best time to start is during your student years. The easiest way is to find a supervisor or a company for an internship already at the university — an experienced employee of the company or university can act as your mentor.
Finding a good supervisor is extremely important! He will help you with the choice of research direction and tell you where to find the necessary resources, guide you throughout your career, share experience, knowledge, and help you make connections.
The science of machine learning provides unlimited opportunities for growth. Here you can develop at any international level all your life, participate in advanced research, create new models that will be used around the world. ML is not just a profession, it is a way of life, so ML is chosen for love.
