I've learned the fundamentals of Python, including dictionaries, conditionals, and similar beginner topics. A friend and I want to build an AI project next year, but I'm not sure what to study next. Should I focus on data structures and algorithms, math, machine learning libraries, or something else? Recommendations for beginner-friendly courses and learning resources would be really helpful. We haven't decided yet whether we want to integrate an existing AI model or train a model ourselves.
4 Answers
If your goal is simply to add AI features to a project, you can start by using an existing model through a library or API. Tools such as LangChain can help connect models to an application, but you should still learn basic Python project structure, error handling, and how to work with data. Creating and training your own model is a separate, significantly deeper path.
Don’t try to jump straight from learning Python syntax into deep learning. A sensible path is to get comfortable writing complete programs, then study data structures and algorithms. After that, learn the core math and explore practical machine learning with tools such as NumPy, Matplotlib, and a framework like PyTorch or TensorFlow.
First, figure out what you mean by an “AI project.” Integrating an existing model into an app is relatively approachable, while training a machine-learning model yourself requires much more preparation. For the latter, build a solid foundation in Python, data structures, algorithms, NumPy, and data visualization before moving on to machine-learning tools.
A practical beginner route is to strengthen Python first, then learn basic statistics and linear algebra, followed by an introductory machine-learning course. Once you understand the concepts, try a small project such as classifying simple data or making a basic prediction model before attempting a large AI application. Courses with titles like “zero to hero” can be useful, but check that they match your current level.
The math matters too, especially linear algebra, probability, and eventually some calculus. Without those basics, many machine-learning concepts can feel like unexplained formulas.