When you're just starting to figure out machine learning, the hardest part is not drowning in the sea of courses and tutorials.
I went through this myself. In the end, I realized that the best approach is one good, systematic course where theory and practice go hand in hand.
🎓 Course by Yuri Kashnitsky - mlcourse.ai - it's a classic. It helped me a lot. It calmly and thoroughly explains basic things: from regression and classification to boosting and ensembles.
There are homework assignments — they help you better understand the topic and reinforce the material. Plus, the materials are available in Russian and English (on Habr and on YouTube), so if you're interested, you can take it in English right away.
Sometimes I review it before interviews — it helps refresh my knowledge.
💡 And another tip: check out ODS.ai
It's a well-known ML community where courses, challenges are held, and you can just meet people who are also learning.
So through collaborative work on projects during online courses, you can build useful connections and become part of the community.
💬 In short, if you're just starting, it's better not to spread yourself thin — pick one thing, go through it to the end, do the homework, and feel free to ask questions in the community.
With that, you can confidently move forward.
P.S.
If you know any other cool courses and materials useful for beginners, share them in the comments!
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