Machine Learning Mastery Program
Join our comprehensive 18-month journey into practical machine learning applications. Starting September 2025, learn from industry veterans who've built real systems at scale.
Reserve Your SpotWhat You'll Actually Build
Forget theoretical exercises. Our students work on real projects that mirror what you'll encounter in the field. By month six, you're deploying models that solve actual business problems.
- Build recommendation systems like Netflix uses for content discovery
- Create fraud detection models that banks actually rely on
- Develop computer vision applications for manufacturing quality control
- Design NLP systems that handle customer service automation
- Deploy models to cloud infrastructure with proper monitoring
- Work with messy, real-world datasets that don't come pre-cleaned
We've structured everything around hands-on learning because that's how you retain knowledge. Each module builds on the previous one, so by graduation you have a portfolio that demonstrates genuine capability to potential employers.
Program Structure & Timeline
Weekly Commitment
Plan for 15-20 hours per week including live sessions, project work, and self-study. Most students find this manageable alongside full-time work with proper time management.
Prerequisites
Basic programming experience in any language (we'll teach Python), high school mathematics, and genuine curiosity about how machines learn patterns from data.
Class Schedule
Two live sessions per week (Tuesday 7PM, Saturday 10AM Taiwan time) plus optional Friday office hours. All sessions recorded for different time zones.
Starting Date
September 15, 2025. Early bird applications close July 1st. We accept maximum 24 students per cohort to maintain quality interaction and personalized feedback.
Your Instructors
Thom Eriksson
Lead ML Engineer
Spent six years at Tesla working on Autopilot perception systems. Before that, built recommendation engines at Spotify. He's the guy who explains complex algorithms using everyday examples that actually make sense.
Viktor Petrov
Data Science Director
Led ML initiatives at three fintech startups, two of which got acquired. His fraud detection work at payment processors helped prevent millions in losses. Viktor teaches the business side of ML that bootcamps usually skip.
Alexei Rosetti
NLP Specialist
Former research scientist at Google DeepMind, now consulting for healthcare companies on medical text analysis. His chatbot work handles customer queries for several major e-commerce platforms across Asia.
Marco Castellano
Infrastructure Lead