Before you tackle ML, make sure you’ve got core Python down—think data types, control flow, functions, OOP and libraries like NumPy/pandas—plus get comfy with version control, testing and interactive platforms to practice. You can also brush up on optional math concepts like linear algebra and stats as needed.
Once you’re solid on Python, dive into machine learning foundations, then level up with deep learning, real-world projects and even a bonus section on LLMs. Build out a portfolio to showcase your work, and if you want guided paths, check out those Datacamp tracks or Tim’s DevLaunch mentorship for hands-on support.
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