S27:E6 - The Crossover of Health, Technology and Art (Daniel Bourke)

May 8, 2024·49 min·3 clips

The science dean called Daniel Bourke in at age 19 after two years of failing biomedical science and asked 'what else are you interested in?' — which led to a food science degree and eventually a machine learning career.

1. CodeNewbie S27:E6 features Daniel Bourke, founder of Mr. D. Burke Studios and the Nutrify food-recognition app, discussing how he combined interests in health, technology, and art into a machine learning career. 2. Bourke grew up in a family with a swim school business, first heard about computers at his grandparents' kitchen in a single-digit age, and persuaded his family to get one — which immediately got its own room in the house. 3. In high school, Bourke built a color-coded Excel hotel booking system with dynamic cell updates, spending an entire semester on what he describes as a 'primitive booking.com.' 4. He enrolled in biomedical science at university without having studied biology in high school, then failed the first two years by prioritizing time with friends over attending classes. 5. A pivotal intervention came when the science dean called him in at 19, asked what he was actually interested in, and suggested he switch to food science and nutrition — a subject Bourke had already taught himself through YouTube videos for his obstacle racing training. 6. After switching degrees, Bourke earned top marks for the first 18 months because he had already self-taught the foundational curriculum, and he reflects that this experience showed him the power of studying what you are already naturally pulled toward. 7. Simultaneously throughout university, Bourke worked at an Apple retail store, starting in the warehouse and progressing to the Genius Bar, where he spent three and a half years and estimates he interacted with approximately 4,500 different customers. 8. He describes the Genius Bar's core meta-skill as explaining technical concepts while empathizing with the person in front of you — adapting communication from a 15-year-old to an 85-year-old who needed every button press narrated. 9. Bourke left Apple in February 2017 to co-found a web startup with a colleague, but the startup failed within months, and by mid-2017 he began his fourth attempt to learn to code, this time using Free Code Camp, Code Academy, and Coursera. 10. He designed a personal system: study Monday to Friday, drive Uber Saturday and Sunday on savings, and work through a self-created 'AI master's degree' blog post he assembled from online resources, with the explicit goal of flying to California for a tech job. 11. In April 2018, after approximately nine months of intensive self-study, a LinkedIn connection named Mike met him for coffee and introduced him to a contact named Cam, who ran a small machine learning company in Australia and asked 'can you start Monday?' on a Thursday. 12. Bourke joined as machine learning engineer number three, was handed a data set on day one with instructions to find something interesting, and describes the moment he realized 'this stuff actually works' as transformative. 13. Throughout the internship, he maintained a habit of spending evenings researching solutions to problems he encountered at work, then returning the next day to apply what he had learned — a cycle he describes as the engine of his rapid progress. 14. In June 2019, Bourke left his machine learning engineering role to go independent, posting a semi-viral article titled '10 things I learned in my machine learning engineering job,' which caught the attention of Zero to Mastery founder Andre from Canada. 15. Andre asked him to create machine learning courses for Zero to Mastery's web development audience, leading to Bourke building a beginner-level course, followed by three comprehensive courses covering topics from foundational machine learning to state-of-the-art deep learning. 16. He identifies the biggest misconception among beginners as believing machine learning requires deep mathematical expertise, arguing that a working engineer's daily practice is approximately 99% Python code and data manipulation using pre-trained models from Hugging Face, Google, or Facebook. 17. Bourke built his courses to reflect this reality — approximately 95% code-first, with external links for those who want underlying mathematics — because his expertise is applying existing frameworks to practical problems, not teaching the mathematics behind them. 18. The episode is a structured linear narrative following Bourke's chronological journey, with host Saran asking detailed clarifying questions at each inflection point to surface the 'why' behind each decision. 19. Bourke's tone throughout is self-aware and low-key, frequently acknowledging imposter syndrome as present but healthy — a signal that you know you have baseline skill and know you could improve. 20. This episode will resonate most with people who are mid-career changers or self-taught learners considering a transition into machine learning or AI.

As heard by us

A grounded CodeNewbie episode about public learning, imposter syndrome, and turning AI study into real machine learning work.

CodeNewbie catches Daniel Bourke at the point where learning machine learning turns from private study into public momentum. He talks about posting what he was learning during 100 Days of Code and AI study, which leads to a LinkedIn coffee meeting, another introduction, and then…

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Turn self-study into a real tech path.

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