Что делать, если ничего не понимаешь на курсе по ML

Pre-register for Stream 5 of the "ML Foundation" course: https://forms.yandex.ru/u/6a16ef41505... Follow MLinside on Telegram: https://t.me/+xPCRRLylQh5lMmI6 If you've started the Machine Learning course and realized after 15 minutes that you don't understand anything, this video is for you. Most beginners think the problem is their own: "I lack talent," "I started too late," or "ML isn't for me." But in practice, almost all specialists have experienced this. Machine learning combines programming, mathematics, statistics, and data science, so at first, the brain simply has nothing to rely on. In this video, Alexander Dubeykovsky (ML Engineer at Avito, former Yandex, MLinside expert) explains why misunderstandings are a normal part of learning, how to properly study complex topics, and what to do if another lesson seems like a jumble of unfamiliar words. Who will benefit from this video? ▪️Those just starting to learn Machine Learning ▪️Students of online courses on ML and Data Science ▪️Developers and analysts transitioning to AI ▪️Those considering dropping out due to complex topics ▪️Anyone who wants to build the right learning strategy If you've ever felt like you "don't understand anything," let me know in the comments which ML topic was the most challenging for you. Subscribe to the MLinside channel and join our Telegram channel—there you can ask questions and discuss learning with other students. In less than a month, we're launching that very same ML System Design course with Valery Babushkin. Sign up now while there's still space! Website with the course program and pricing: https://mlinside.ru/system-design/?ut... Pre-registration form: https://forms.yandex.ru/u/6a0436bd902... Timecodes: 0:00 – Introduction 0:22 – Why the ML course might be confusing 2:35 – A story about SHAD 5:30 – One of the biggest mistakes beginners make 6:14 – A story about a friend 7:19 – Alexander's story 8:24 – What to do if the lesson isn't clear 8:43 – First step 9:02 – Second step 9:32 – Third step 9:57 – What to do if this didn't help 10:45 – About the main problem 12:41 – Word From Viktor Kantor 1:17 PM – Conclusion #machinelearning #ml #datascience #ai #python #mlinside #training #data_science #artificialintelligence #mlengineer