3 Machine Learning Myths That Are Stopping You From Getting Started
📌 Fill out the form now and receive the "15 Most Common Interview Questions and Answers" checklist.
Pre-registration form: https://vk.cc/cO375f
"ML Database" course: https://clck.ru/3Q7Asj
"ML in Business" course: https://clck.ru/3Q7Asj
Telegram MLinside: https://t.me/+xPCRRLylQh5lMmI6
Haven't you started learning machine learning yet because you think it's "only for geniuses"?
In fact, most of the barriers are imaginary. In this video, Andrey Zhogov (ML engineer at Sberbank, lecturer at the Moscow Institute of Physics and Technology, and MLinside mentor) examines the three most harmful myths that prevent people from entering the field of data science.
In this video:
• "You need to be a mathematical genius to do ML"
• "ML requires a powerful computer"
• "ML is all about neural networks and ChatGPT"
• What an ML engineer's first job really looks like
• Why understanding basic algorithms and being able to apply them is enough
• What a "toolbox" mindset means and how it changes your learning approach
Suitable for those who:
• are afraid they won't be able to handle math;
• are putting off learning ML due to "insufficient hardware";
• want to start a career in data science but don't know where to start.
Speaker: Andrey Zhogov, ML engineer at Sberbank, lecturer at the Moscow Institute of Physics and Technology, and MLinside mentor.
Our "ML Basics" course helps beginners move from theory to practice and land their first job in machine learning.
#machinelearning #datascience #mlinside #career #mlforbeginners
If you liked the video, give it a like and subscribe to the MLinside channel. They publish honest videos about machine learning, careers, and real stories of those who entered data science from scratch.
Timecodes:
00:00 — Introduction: Why many are afraid to start learning ML
01:45 — Myth #1: "You need to be a mathematical genius to do ML"
02:32 — Myth #2: "ML requires a powerful computer"
03:46 — Myth #3: "ML is all about neural networks and ChatGPT"
05:04 — What an ML engineer's job really looks like
07:32 — Conclusion: What you need to really get started in ML