Python Skills You NEED Before Machine Learning

Thinking of diving into ML? First, nail core Python—variables, functions, OOP and modules—then level up your data handling game with pandas and NumPy. Use interactive tools like Jupyter, practice Git and CLI, and pick up essential software-engineering habits (testing, virtual environments). A bit of optional math (stats and linear algebra) closes gaps before jumping into ML frameworks.

Once you’ve got the foundation, tackle machine-learning basics with scikit-learn, move on to deep-learning libraries like TensorFlow or PyTorch, and learn how to productionize real-world pipelines. Bonus: explore LLMs and build a standout project portfolio. For guided learning, check out the creator’s recommended DataCamp tracks (with a discount) or join DevLaunch mentorship for hands-on support.

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