If you are considering pursuing a Ph.D. in machine learning, or just getting into the field without a “proper" background”, here are some important insights you should know before making a decision.
Academia vs. Industry:
Pursuing a Ph.D. in machine learning can be a great way to break into the field and achieve specific research goals.
However, it is important to consider the pros and cons of academia vs. industry. Alternative pathways like joining open-source research organizations or working on side projects might actually prove to be more useful than graduate studies for some individuals.
Here is a clear list of some of the best tips from Brian’s interview:
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Personalizing resumes: When it comes to landing your first job in machine learning, it is crucial to showcase the quantifiable impacts you have made. This could be speeding up a service, improving a model's performance, or generating revenues for an employer. Personalizing your resume to highlight these achievements is key.
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The power of brand and online presence: Don't underestimate the influence of a strong personal brand and online profile when it comes to recruiting. Share your work, collaborate, and use social media to your advantage.
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Build connections: Networking is essential in any field, and machine learning is no exception. Dive into old-school social networking and start making connections in the industry. Reach out to people on LinkedIn, attend networking events, join online communities, and go the extra mile to stand out in a competitive market.
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Share a story: Once you get into the interview, it is important to share your story. Recruiters have read your resume, so talk about a project you worked on and the challenges you surpassed. Anything that makes you, you!
These insights were shared by Brian Burns, Ph.D. candidate at the University of Washington and founder of the AI Pub Twitter account. To learn even more insights on how to get into AI, grow a Twitter page, host a podcast, ace interviews, and build a better resume, tune in to the full podcast below!
Watch the Podcast
https://youtu.be/M0aLFZegaec?embedable=true&transcript=true
(also available on Spotify or Apple Podcasts).