Using AI to Learn Math and Coding

As a complete beginner in Python and coding, I’ve realized how powerful AI can be in helping me grasp challenging concepts. This week in BALT 4363, we explored how AI tools, especially ChatGPT, can guide our learning, particularly in applied math for machine learning (ML). For instance, I learned how to break down complex math topics, like linear algebra, probability, and calculus, into bite-sized, practical pieces using resources tailored for ML applications. Instead of trying to memorize every formula or process, AI can suggest relevant resources and examples, which makes it easier to understand how these concepts work in real-world scenarios.

AI is also great for developing a personalized learning plan, especially when you're feeling overwhelmed. One of the key takeaways from this week was how I can use AI to structure my study routine, balancing my focus between linear algebra (which is essential for understanding data structures in ML) and probability/statistics (which help with decision-making processes in models). By focusing on applying these concepts to actual ML problems, like linear regression or neural networks, I can see the math in action instead of just theoretical equations. This hands-on approach makes learning much less intimidating and more exciting.

Lastly, I’ve learned that Python isn’t just for coding—it's a vital tool in understanding and applying these math concepts. As I continue to practice Python, I realize that it can be used to implement these mathematical theories in ML models, helping me understand their real-world value. The more I explore Python libraries and AI tools, the more confident I become in my ability to use them effectively in future projects. In short, AI is not only helping me with coding but also breaking down complex math in a way that feels manageable and fun.