Python Skills You NEED Before Machine Learning

If you’re gearing up for ML, start by nailing core Python: variables, loops, functions and OOP, then dive into data handling with pandas and NumPy. Use interactive platforms (like Jupyter) and pick up essential SWE tools (Git, testing, virtual envs). Optional math refreshers on statistics and linear algebra will make foundations rock-solid.

Next, build on that with ML basics (scikit-learn), explore deep learning frameworks, and tackle real-world projects to grow your portfolio. Bonus round: get hands-on with LLMs and consider mentorship (DevLaunch) or beginner-friendly Datacamp tracks—grab 25% off via the exclusive link!

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