What's the Best Way to Learn AI?
It Depends on You.

There's no universal 'best'—it depends on your background, learning style, and goals. Self-study works if you're disciplined and have strong fundamentals. Courses provide structure but move at a fixed pace. Bootcamps suit career changers who need full immersion. 1:1 coaching offers the fastest path for developers who want personalized guidance. The best approach matches your constraints: time, budget, and how much accountability you need.

Self-study is free and flexible but has a 5-10% completion rate for a reason—it requires exceptional discipline. Structured courses provide deadlines and community but often teach at a pace that's too slow or too fast for you. The middle ground: use courses for foundational concepts, then self-study for projects. Or get coaching to create a custom curriculum that moves at your exact pace.

Bootcamps ($10K-$17K) make sense for career changers starting from zero who need full immersion and job placement support. For developers or technical professionals, you're often paying for material you could learn faster independently. The cohort pace doesn't adapt to what you already know. Consider alternatives like targeted coaching or structured self-study with mentorship.

Coaching gives you a custom learning path based on your existing skills and specific goals—no wasted time on what you already know. You get immediate answers when stuck instead of spending hours debugging alone. Most importantly, you have accountability. Someone is tracking your progress and adjusting the plan when life gets in the way. It's the fastest path for people who value time over money.

Depends entirely on your starting point and depth. Basic ML concepts: 1-2 months. Building production applications: 3-6 months. Becoming job-ready as an AI engineer: 6-12 months. These timelines assume consistent, focused effort. Most people dramatically underestimate the time because they don't account for getting stuck, losing motivation, or relearning concepts they didn't fully grasp.

At minimum: basic Python programming and high school math. Helpful but not required: linear algebra, statistics, and software engineering experience. The more programming experience you have, the faster you'll progress—AI engineering is mostly engineering, not math. If you're missing prerequisites, address those first. Trying to learn AI without Python fundamentals is like learning to run before you can walk.

Not necessarily. While some programming experience is helpful, many of my clients have successfully transitioned from web development, data science, or other technical backgrounds. We'll assess your current skills during our strategy call and create a personalized plan that meets you where you are.

Most clients invest 10-15 hours per week, but this can be flexible based on your schedule. We'll have weekly 1:1 calls plus time for you to work on projects and learning. The key is consistency. Regular, focused effort beats occasional marathons.

You become a member of the AI Native Engineer community, and you stay and keep going. Career transitions take different amounts of time for everyone, and I'm not going to abandon you if things take longer. You get ongoing support through good times and bad.

Online courses give you content. 1:1 coaching gives you a personalized roadmap, direct feedback on your work, career strategy, interview prep, and accountability. You get answers to your specific questions and guidance tailored to your unique situation instead of generic advice meant for everyone.

Investment details are discussed during the 30-minute strategy call, where we'll assess your goals and create a custom plan. The program is designed to pay for itself quickly through your increased salary. Most AI engineers see a 20-50% pay increase.

Absolutely. Most of my clients work full-time and make steady progress. We'll schedule calls at times that work for you and create a realistic plan that fits your schedule. Consistency matters more than intensity.