I've learned the basics of Python so far, including dictionaries, conditionals, and similar introductory topics. A friend and I would like to build an AI-related project next year, but I'm not sure what to study next. Should I focus on data structures and algorithms, mathematics, machine learning libraries, or something else? Beginner-friendly courses and learning resources would be especially helpful.
4 Answers
For creating models, you’ll eventually want NumPy, data visualization tools such as Matplotlib, and a framework like PyTorch or TensorFlow. You should also learn some linear algebra, probability, statistics, and basic calculus. You don’t need to master all the math immediately, but without those foundations, many machine-learning concepts can feel like unexplained formulas.
A practical route is to keep building small Python projects while gradually studying the fundamentals. Once you’re comfortable with the language and basic algorithms, try an introductory machine-learning course, then move on to deep learning if your project needs it. Hands-on beginner courses can help you start building sooner, but they’ll be much easier to follow if you already understand the basics of Python, probability, and linear algebra.
It helps to clarify what you mean by an AI project. If you only want to add an existing AI model to an app, you can probably get started with an API and learn the relevant libraries as you go. If you want to train or build a machine-learning model yourself, that’s a much longer path and requires more preparation.
Before jumping into machine learning, build a stronger programming foundation. Get comfortable with functions, modules, classes, file handling, debugging, and writing small projects. Then study data structures and algorithms. That will make it much easier to understand the code and libraries used in AI.
That makes sense. I’ll work on Python, data structures, and the math foundations before trying to build the full project.