I'm a second-year student specializing in AI and machine learning, but I'm not sure where to begin. I want to learn Python, data structures and algorithms, AI, and machine learning, but my programming experience is still very limited. I understand basic ideas like arrays, but I'm not yet comfortable writing code. Should I start with a learning platform such as CodeChef, HackerRank, or LeetCode? What order should I follow, and what would a realistic beginner-friendly roadmap look like?
4 Answers
Begin with a structured Python course from a resource such as freeCodeCamp or Codecademy. Learn the syntax and core programming concepts first, then make a few simple programs of your own. After you feel comfortable coding, choose one DSA practice platform instead of trying to use all three. Add machine learning gradually through small projects rather than worrying about an elaborate roadmap from day one.
An AI tutor can help explain Python concepts, suggest practice exercises, and generate small projects that match your level. Just use it as a guide rather than copying its solutions blindly: try the task yourself, ask for hints, and make sure you can explain and debug the final code. Once your fundamentals are solid, you can use the same approach to explore AI and ML. The key is to keep building things instead of only collecting tutorials.
If you’re still struggling with basic programming, jumping straight into LeetCode will probably be frustrating. First learn how to write, run, read, and debug simple Python programs. DSA platforms are much more useful once you already understand the language; they mainly help improve problem-solving and interview-style skills rather than teach programming from scratch.
That makes sense—I’ll build up the basics before attempting those problems.
Don’t try to learn Python, DSA, AI, and machine learning all at the same time. Start with Python fundamentals: variables, conditionals, loops, functions, lists, dictionaries, sets, modules, exceptions, file handling, and basic object-oriented programming. Build a few small projects so you’re actually writing and debugging code instead of only watching tutorials. Once you’re comfortable with Python, study data structures and algorithms and use one practice site, such as LeetCode or HackerRank, to reinforce the concepts. After that, learn the math and tools behind ML, including NumPy, Pandas, data visualization, statistics, and probability, then move on to scikit-learn and practical projects. These websites are practice tools, not replacements for learning the underlying ideas, so focus on understanding your solutions rather than completing hundreds of problems.
Thanks, I’m starting with Python basics and a small project now.