Скачать или смотреть Machine Learning with NO Tech Background? Marina’s Guide to Breaking In | Ep30

Subscribe to our newsletter: https://dataneighbor.substack.com/

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 (‪@MarinaWyssAI‬). 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 Episode

Marina’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 Nelson

Courses:

Machine Learning Specialization by Andrew Ng (Coursera)
Deep Learning Specialization (deeplearning.ai)
Math for Machine Learning (Three Blue One Brown on YouTube)

Chapters

0:00 Trailer
2:00 What “Gratitude-Driven” Really Means
4:25 From Political Science to Machine Learning
7:00 Self-Study Strategies and Choosing the Right Resources
10:55 How Marina Landed Her First ML Job
14:30 Frameworks for Promotions and Career Planning
17:00 Using AI Tools for Learning and Productivity
21:00 How to Know You’re Ready to Move to the Next Level
24:40 First Projects and Learning by Building
29:00 Depth vs Breadth in ML Skill-Building
34:00 Overcoming Mental Barriers and Imposter Syndrome
38:45 Breadth Roadmap: What Beginners Should Learn
42:30 Top Courses, Books, and Resources
47:00 Final Advice and Underrated Skills

Connect with Hai, Sravya, and Shane (let us know YouTube sent you!):
Hai:   / hai-guan-6b58a7a  
Sravya:   / sravyamadipalli  
Shane:   / shaneausleybutler  

#machinelearning #datascience #careertransition #gratitudedriven #selfstudy #ai #deeplearning #python #coursera #careerroadmap #productivity #chatgpt #learnML #dataeducation #DataNeighborPodcast #nontechtotext #womenintech #mlprojects