Ever wondered how someone with a political science degree ends up doing machine learning at Twitch? Meet Marina Wyss - applied scientist, blogger, YouTuber, and all-around productivity and learning expert. In this episode of the Data Neighbor Podcast, Marina shares her unconventional journey into tech, how she self-taught herself machine learning, and why her mantra of being “gratitude-driven” is her antidote to hustle culture.We dive into:- How Marina transitioned from political science and jewelry management into data science and ML.- Her self-study roadmap: From free courses to Coursera to deep technical books.- Practical frameworks for self-learning, getting promotions, and breaking into the ML industry.- Why she rejects hustle culture in favor of a gratitude-driven approach to productivity.- How she leverages AI tools like ChatGPT and Replit to accelerate learning and personal projects.- Common mistakes early learners make and how to avoid being overwhelmed.- Her take on Python vs R, what to focus on when starting ML, and why building your own projects is essential.Whether you're coming from a non-technical background, looking to break into ML, or trying to navigate learning in the age of AI, Marina’s story and advice will inspire you to take the leap - and build the skills that matter.

Links Mentioned in the EpisodeMarina’s Blog: https://www.gratitudedriven.com/Books Mentioned:- Designing Machine Learning Systems by Chip Huyen- AI Engineering by Chip Huyen- Software Engineering for Data Scientists by Catherine NelsonCourses:- Machine Learning Specialization by Andrew Ng (Coursera)- Deep Learning Specialization (deeplearning.ai)- Math for Machine Learning (Three Blue One Brown on YouTube)

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