
What advice would you give to an undergraduate attempting to break into ML research right now?
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Jayden S. asked a question to Kelly W.
Category: Career Advice
Date asked: Friday, July 18, 2025
Last reviewed: Monday, July 21, 2025
Kelly W.
research scientist
Great question! I think the most important thing is to Build a Strong Math and Programming Foundation. This includes:
- Master the Fundamentals: A solid grasp of linear algebra, calculus, probability, and statistics is crucial. Many online courses (Coursera, edX, Udacity) offer excellent resources.
- Learn Programming: Python is the dominant language in ML. Become proficient in it, including libraries like NumPy, Pandas, and Scikit-learn.
- Understand ML Concepts: Take introductory and advanced ML courses.
In addition to that, it is important to Gain Hands-on Experience through
- Personal Projects: Work on your own ML projects. This demonstrates initiative and allows you to apply your knowledge.
- Contribute to Open Source: Contributing to open-source ML projects is a fantastic way to learn from experienced developers and build your Github portfolio.
- Industry Internships: Seek internships at companies or research labs working in ML.
- Research Opportunities: Leverage resources and network at your school and find opportunities to work with professors or grad students on ML research projects.
Monday, July 21, 2025
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