I filmed this one from Athens, Greece and broke down what the last year of learning AI and machine learning has actually looked like for me in my 30s. I’m not trying to pivot into data science, but I do think understanding how this stuff works is going to massively help your future tech career—especially as AI replaces a lot of tasks and changes what “junior” roles look like. The big reason I started was pure curiosity: I wanted to know what’s happening in that “in-between” step when you prompt an LLM and it spits out code. Once you understand the basics, it’s honestly empowering because you also see the limitations. I share the roadmap I’m using: start with foundations instead of getting lost in endless libraries. For me that’s Python (the community standard) plus the math under the hood (linear algebra, calculus, probability/stats—high school level to start). Then I focused on core concepts like supervised vs unsupervised learning, regression vs classification, and the practical tooling (NumPy, scikit-learn, matplotlib), plus data prep, model selection, and evaluation. I also talk about avoiding tutorial hell—just build projects with real datasets (I used Kaggle and football data), and incrementally increase complexity. Finally, I compare ML to software engineering: less “recipe,” more experimentation, slower feedback loops, and a lot more data plumbing than people expect.