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Let's Talk About Your ML Journey
Whether you're curious about our programs or ready to dive in, we're here to help you figure out the next step that actually makes sense for where you are right now.
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L39.6, khu Cityland, 18 Đ. Phan Văn Trị, Phường 10, Gò Vấp, Thành phố Hồ Chí Minh 700000, Vietnam
How We Got Here
2022
Started Small
Three ML practitioners who kept getting asked the same questions decided to create something different. We wanted to teach people how machine learning actually works in practice, not just theory from textbooks.
2023
First Real Programs
Launched our foundational course with 15 students. We learned as much from them as they did from us. Turned out people really appreciated the hands-on approach and real project work over endless PowerPoint slides.
2024
Building Momentum
Expanded to multiple tracks and brought in more instructors who actually build ML systems for work. Started seeing our graduates land positions at companies doing interesting things with data.
2025
Where We Are Now
Running cohorts throughout the year with a solid curriculum that keeps evolving. Our next intake starts in September 2025, and we're constantly refining based on what the industry actually needs.
Who'll Be Teaching You
Our instructors aren't just teachers. They're practitioners who work with ML systems daily and understand what skills translate to actual work.
Astrid Bergqvist
Lead ML InstructorSpent eight years building recommendation systems before deciding to teach. Has a knack for explaining complex concepts without making you feel lost.
Niko Virtanen
Deep Learning SpecialistWorks on computer vision projects during the day, teaches neural networks at night. Makes sure you understand the math but doesn't bore you to death with it.
Elara Thornley
Data Engineering MentorBelieves that good ML starts with good data pipelines. Teaches the unglamorous but essential stuff that keeps models running in production.
Our Teaching Philosophy
We focus on building understanding through projects, not memorization. You'll work on real datasets, debug actual problems, and learn to make decisions that matter when deploying models. Theory is important, but only when it helps you do better work.