I'm getting into coding because I've always been interested in it. I've watched videos about neural networks and other areas of AI, and the subject looks fascinating. My school is currently teaching basic Python, and I'm wondering how difficult it would be to start learning neural networks and what programming, math, and other foundations I should study first. I'll be entering my junior year of high school, so I haven't taken calculus yet.
4 Answers
Neural networks can become graduate-level material when you study the theory deeply, but you can absolutely experiment with simple models as a high school student. Begin with a beginner-friendly machine learning project, such as recognizing handwritten digits with the MNIST dataset. That will introduce you to training data, predictions, loss, and model evaluation without requiring years of preparation. Treat it as a project to learn from, not something you need to master all at once.
Start by getting comfortable with programming fundamentals rather than jumping straight into advanced AI. Python is the best first language because it’s widely used for machine learning and has excellent libraries. Build small projects so you learn variables, functions, loops, data structures, debugging, and eventually object-oriented programming. Once those feel natural, you can explore other languages if you have a reason to use them. Math matters too, especially algebra, functions, probability, statistics, and matrices. Calculus is helpful later, but you don’t need to wait until you’ve taken it to begin learning.
What specific math should I focus on right now? Since I’m going into my junior year, I haven’t studied calculus yet. Also, how strong should my coding skills be before I start neural networks?
A lot of beginners skip the math and then get confused when the concepts stop making sense. You don’t need advanced math immediately, but learning the basics alongside coding will make things much easier.
Don’t measure your progress by whether you understand every neural-network video. Pick a small project, such as classifying images or predicting values from a dataset, and work through it slowly. Use Python and common machine-learning tools, but make sure you understand what the code is doing instead of only copying tutorials. Consistent practice and curiosity are more important than already knowing calculus or several programming languages.
A useful progression is basic Python, then data handling and visualization, followed by introductory statistics and regression. Learn what averages, distributions, correlation, probability, and model fitting mean. Linear and logistic regression are especially valuable because they teach many of the ideas that appear again in neural networks. For math, focus first on algebra, functions, vectors, matrices, and basic statistics. You can add calculus and linear algebra more formally as you advance.
That sounds like a good starting project. I was worried I needed to understand all of AI before trying anything practical.