I’m a biologist turned data scientist. Like many of you, I learned machine learning online through courses and books, practicing my skills in data competitions. Eventually I got my first job as a data scientist in finance. I was over the moon. That was more than 10 years ago.

In that role, I realized that beginner courses are great for starting out but often miss real-world applications. I spent hours online searching for best practices and information for my projects. I figured if I was struggling, others must be too. What if I gather what I learned in an online course? So I did, and Train in Data was born.

Since then, I’ve created more courses with colleagues who shared similar journeys. In our courses, we cover essential aspects of the machine learning workflow, such as model deployment, feature engineering, feature selection, how to work with imbalanced data, forecasting and more.

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