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Do you already have some background in programming or math, or are you starting from scratch?
It depends on that, because the path changes a bit. If you already code, jump straight into platforms like Coursera or edX where there are free courses (or you can audit without paying). Andrew Ng has a classic machine learning course that people recommend a lot. There's also Fast.ai, which is more practical and less theoretical - they start with actual code from day one. YouTube has decent channels too, though you have to be more selective there because there's a lot of noise.
If you're starting from zero, first you need Python (it's the standard in this field). Learn the basics with free resources like Codecademy or just watch tutorials. Once you're comfortable with lists, functions, and that stuff, then move on to libraries like NumPy and Pandas. After that, you can get into scikit-learn for your first machine learning models. The idea is not to skip steps because otherwise you'll get frustrated.
The important thing is to actually build projects while you learn, not just watch videos. Find datasets on Kaggle (free too) and try to build things even if they're simple. That's the only way you'll really understand what's going on behind the code. And double-check the sources you use; there's a lot of outdated or straight-up wrong content floating around.
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