🤓 Math Under the Hood #MLSTART

Many people try to jump straight into models and coding, but at some point you still hit a wall where you can't move forward without math.

You don't have to dive deep into theory, but understanding why something works is important. Understanding how ML algorithms work will help you choose the right tools for the task, rather than "shooting sparrows with a cannon".

📖 To refresh the theory, I personally use Yandex's workbook. It's a neat, structured breakdown of basic machine learning concepts. Types of tasks and models, training and overfitting, regularization, metrics and loss functions - it's all there.

🎓 If you want to dig deeper, there's Vorontsov's course "Mathematical Foundations of Machine Learning" (MIPT): there's even more linear algebra, probability theory, optimization - in short, the basics. A lot of math, but if you figure it out and understand it, you can train a strong intuition that will come in handy when solving ML problems.

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