Calling sklearn or PyTorch APIs and using pretrained models seems easy, but digging in to develop a deeper understanding of how methods work, when to apply them, and how to push their limits can be daunting and confusing.
Having gone from research to industry, I have successfully guided interns, bachelor/master graduates, and Ph.D. students on their path through the field of machine learning from concepts to applications and back.
Currently, I work as co-founder and principal engineer at an early-stage machine learning start-up coming out of my colleagues and my academic research. I am also very happy to talk about product-market-fit, validation, business models, start-up fundraising and bootstrapping, and all the things related to the pre-seed/seed stage.
Prior to starting my company, I worked as a machine-learning research scientist bringing deep learning methods on tabular data to know-your-customer and anti-money-laundering products in financial institutions.
Before that, I completed my PhD where I developed machine-learning algorithms for reverse engineering software and applied them on software engineering and network analysis tasks.
Throughout my career, I've done commercial data science consulting projects on the side.
What students say
Core Skills
Coding Deep learning Seed-stage Pytorch Scikit-learn Machine learning Natural language processing Sklearn Nlp Bootstrapping Entrepreneurship Data science Artificial intelligence Data analytics Phd supervision Data analysis Statistics Spin-offs Startups Pre-seed Data Python Fundraising Product market fitMentorship Program
$240/month
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