How can you start a successful career in Machine Learning in Europe?

Start by pursuing a specialized degree or certification in fields related to machine learning, such as data science or computer science. Concurrently, immerse yourself in hands-on projects, which could involve participating in online challenges or contributing to open-source projects.

Focus on creating projects that solve practical problems, utilizing popular datasets or even your own unique data. Engaging in competitions on platforms like Kaggle or contributing to open-source projects on GitHub can provide valuable experience. Document each project comprehensively, detailing the problem, your approach, the machine learning techniques used, and the outcomes.

Engage actively in specialized online communities like GitHub or Kaggle, where you can collaborate on projects and participate in discussions. LinkedIn is a valuable tool for connecting with industry professionals and joining relevant groups. Attend industry events, like AI conferences or local tech meetups, to interact with peers and experts. These settings provide an ideal platform to exchange insights, seek advice, and stay updated on industry trends.

To start an ML career, you need a perfect balance between learning and networking. Let me explain: An ML engineer not only needs to understand statistics and software engineering, but he/she also needs to understand a fundamental concept: He's nobody in the market. No company will hire a freshly started ml engineer. However, being in constant touch with people from the market will teach him what to learn, what the market needs, and how to present himself during interviews. The central point between learning and market needs is the portfolio. Any ml engineer should have one, with real case projects that companies are willing to pay someone to solve.

In my experience, the easiest path to getting an established career in ML is to come in through a side door. What I mean by that is to start working as a tangential role, such as Data Engineering or Software Engineering. Because as a new ML professional, you aren't going to be very productive, so no one wants to hire you. But if you come with other supporting skills that are helpful to the team, then it can help offset your newness in ML.

It's not enough to simply have the skills for the role. You have to be able to do well in the interview or even attract employers in other ways. In light of this, there's really two steps to getting an ML job: 1.Get machine learning skills — technical skills, coding, software engineering, data handling. 2.Analyze Market in machine learning skills — non-technical skills, communication, interviewing, portfolio creation. The first lets you do the job but it's the second that helps you get hired.