How do I actually learn AI/ML deeply enough to build systems (not just follow tutorials)? [D]

A Reddit post in r/ArtificialIntelligence has gone viral, capturing a frustration familiar to many aspiring AI engineers: the feeling of being stuck in a loop of consuming tutorials without gaining the ability to build real systems. The user, u/Both_Fig_192, describes having surface-level understanding but being unable to apply knowledge independently. They are overwhelmed by the sheer breadth of topics—machine learning, LLMs, agents, frameworks—and unsure how deep to go into math, models, or systems. The core question is how to transition from a passive learner (following tutorials) to an active builder who can think like a developer or researcher.

The post asks specific questions about structuring learning from basics to real-world systems, what to focus on vs ignore early on, and how to practice 'building thinking' instead of just watching videos. Many commenters share their own experiences and advice, emphasizing project-based learning, mastering fundamentals like linear algebra and calculus, and avoiding framework hopping. The thread reflects a widespread need for better learning pathways that bridge theory and practice. For tech professionals, this highlights that even with abundant AI content, the hardest part remains the leap from consumption to creation—and that structured, deliberate practice is the key to building production-ready systems.