You spent a lot of time trying to learn Machine Learning. You read a lot of books, you watched various MOOCs, and you earned a lot of certifications. But despite all this effort, you still haven’t applied to your dream job, nor started to build your portfolio. You feel you haven’t learned anything, and you think the solution is to read another book or earn another certification.
If you identify yourself with this situation, you may be suffering from the impostor syndrome. The impostor syndrome is a psychological phenomenon in which we have a distorted view of ourselves. People who suffer from this syndrome tend to diminish their accomplishments and their merits, leading them to an irrational fear of being discovered as an impostor.
The impostor syndrome is very harmful to experienced developers who are learning Machine Learning because it causes complete paralysis. Because we are afraid of being discovered as frauds, we stop to share our knowledge, we stop to apply to new jobs, or we even stop trying to build projects.
And when we are paralyzed by the imposter syndrome, the biggest mistake we can make is to think we need more knowledge. We believe we need another book, another MOOC, or even a new degree, trapping ourselves in this infinite loop of fear and paralysis.
This paralysis is very harmful to us. Because we are afraid to expose our work, nobody will ever know about our existence. And our objectives with Machine Learning depends on other people to know us. We need recruiters to find us and invite us to interviews, we need users for our products or even the revision of our peers to succeed in our careers.
So, if you don’t want to be stuck in your career, you cannot succumb to the impostor syndrome. You must be conscious of your fear and take little actions that can help you to recover your confidence. Keep reading to learn how.
How to fight the impostor syndrome
The best way to stop feeling like an impostor is to stop thinking like an impostor. You have to be comfortable with the gaps in your knowledge, and you cannot be afraid of exposing these gaps. But to be comfortable with your weaknesses, you must be confident in strength. In other words, you must be satisfied in what you already know.
That’s why it is essential to work on projects and show your results. To gain confidence in what you already know. But if you try to start a big and complex project, you will be terrified once again by the gaps in your knowledge, and you will drop the project. Instead, begin with small and targeted projects, so you can complete them and show your results. In this way, you will help people that know less than you, and you will build the confidence you need to make yourself comfortable with your knowledge gaps.
If you are struggling to build your Machine Learning project, you may be suffering from imposter syndrome. You may already read books or took courses or even went to a master’s degree, but you never practiced with real tools or deployed a model. In either case, a lack of knowledge is not your problem. Your problem is a lack of action towards your project.
So, instead of reading another book or taking another course, I invite you to make your first step. Click on the link below and spend only 5 (five) minutes on building your first Machine Learning model with real tools:
It’s essential to start with a small project or example, so you can complete it quickly and gain confidence. Then you can gradually add complexity in your projects until you begin building real-life applications.