Before jumping into ML, nail down key Python chops—from core syntax and data handling (think pandas, NumPy) to interactive learning tools and software-engineering best practices. If you’re up for it, brush up on the math behind algorithms, then dive into machine-learning foundations, deep learning basics, real-world ML workflows and even a bonus on LLMs. Finally, cap it off by building projects to showcase in your portfolio.
For guided learning, the author calls out two beginner-friendly DataCamp tracks (with a sweet 25% off link) and offers a DevLaunch mentorship program for hands-on projects, real accountability, and job-landing strategies.
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