I remember when I first got into AI, it felt like I’d opened a door into a room full of even more locked doors. 😅 But that’s totally normal. One thing that helped me early on was realizing that AI isn’t just about algorithms or coding—it’s about understanding how machines interpret the world. If you break it down, the fundamentals usually start with learning the difference between supervised and unsupervised learning, how decision trees or regression models work, and also how data is cleaned and used to train models.

Honestly, what made a huge difference for me was finding a structured program that actually explains things step-by-step, in a way that’s practical. This course here really helped me get a grip on it: https://www.advisedskills.com/artificial-intelligence/artificial-intelligence-practitioner It focuses on AI fundamentals, core concepts, and then moves into building and deploying actual models. What I liked is that it didn't just throw Python code at me—it explained the why behind each technique.

And it’s not just theory; you get to build solutions and understand how to apply them in business or real-life scenarios. That kind of hands-on structure helped me finally connect all the random pieces I’d been trying to put together on my own.