TL;DR: Before you even touch machine learning, get really solid on core Python—think variables, data structures, functions and classes—then level up your data handling game with NumPy, pandas and plotting libraries. Don’t skip interactive resources (like the recommended DataCamp tracks), version control, testing, virtual environments and a bit of optional math (linear algebra, stats) to stay sharp.
Once that foundation’s rock-solid, dive into ML basics (scikit-learn), deep learning (TensorFlow/PyTorch), real-world ML workflows and even LLMs. Cap it off by building and sharing actual projects in your portfolio. If you want extra guidance, check out the exclusive DataCamp links and the DevLaunch mentorship program mentioned in the video.
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