These days you can’t just jump into ML without a solid Python foundation: nail the basics (variables, functions, classes), get comfy with data handling (NumPy, Pandas), learn core SWE tools (git, testing, CLI), and brush up on optional math. Then move on to ML fundamentals, deep learning, real-world pipelines, bonus LLM insights, and cap it off by building a standout portfolio.
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