Should I learn Python or R first for machine learning?

Learn Python. Unless you're already deep into academic statistics, then maybe R. But for almost everyone starting out, Python is the right call. Here's why. Python has become the default language for machine learning. The ecosystem is massive. Libraries like scikit-learn, TensorFlow, and PyTorch are all Python-first. When you're stuck at 2 AM with a cryptic error message, you'll find ten times more Python ML help on Stack Overflow than R help. That matters more than you think. R is fantastic for statistical analysis and data visualization. ggplot2 produces beautiful charts with very little code. If you're a researcher analyzing experimental data, R might actually serve you better. I've seen biologists and psychologists do incredible work entirely in R. But here's the practical reality. Most machine learning jobs expect Python. Most tutorials assume Python. Most deployed ML systems run on Python. Starting with R means you'll eventually need to learn Python anyway. One tip I wish someone had given me: don't try to learn both at once. Pick Python, commit to it for six months, build three real projects. After that, picking up R basics takes a weekend. The concepts transfer. A random forest works the same way regardless of syntax. The hard part of machine learning isn't the language — it's understanding why your model is overfitting, how to clean messy data, and when to use which algorithm. Those skills are language-agnostic.