I've been diving into AI and machine learning with Python for a bit, but honestly, I'm feeling a bit lost trying to find the right path. There's an overwhelming amount of resources out there—courses, tutorials, and roadmaps—but it's tough to determine what truly helps in developing practical skills. Recently, I've been considering more structured approaches that provide a clear roadmap, hands-on projects, and guidance. However, I'm still unsure if that's a better option than self-directed learning. For those who have gone through this phase, what really made a difference for you? Did you stick to self-learning, or did having guidance help you progress faster? I'd love to hear your insights.
2 Answers
Honestly, tackling real problems and building things that actually work has made a bigger difference for me than any structured roadmap ever could. I suggest picking a problem that you're passionate about and just dive in. You'll learn way more by wrestling with the challenges that come up along the way!
Are you looking to use AI to enhance your coding skills, add features, or are you interested in creating AI from the ground up?
I'm more focused on truly learning AI and ML as a skill rather than just using it as a tool. I'm still figuring out whether to delve deeper into building models or applying them in practical projects. Any suggestions on which area would be a better starting point?
That's such a valid point! I've noticed that building something from scratch teaches you a lot more compared to just following a set of instructions. The only tricky part for beginners is deciding what to build and not getting overwhelmed. That's why I've been looking for structured ways to have some guidance while still learning by doing.