These are the essential building blocks you’ll need before diving into ML: strong core Python skills (data types, control flow, OOP), hands-on data wrangling and analysis (Pandas, NumPy), plus interactive learning with notebooks and resources like DataCamp’s Python Data Fundamentals and ML Scientist tracks (grab 25% off with the exclusive link). You’ll also want to get comfortable with core software-engineering tools (git, testing), optional but helpful math fundamentals, and a solid grasp of machine learning foundations and deep-learning basics before tackling real-world projects and LLMs.
If you’re looking for extra accountability and real-world guidance, check out DevLaunch’s mentorship program. It’s all about going beyond tutorials—building projects you can showcase, landing that dream job, and getting hands-on support every step of the way.
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