Build Real Models That Actually Work
Most machine learning courses teach theory you'll forget in a week. We focus on hands-on projects that mirror actual industry challenges. You'll spend more time coding than watching slides.
Explore Our Programs
Months Average Learning Path
Real Industry Projects
Completion Rate 2024
Alumni Network
Three Tracks, One Goal
We don't believe in one-size-fits-all education. Pick the path that matches where you're starting from and where you want to go.
Foundation Track
Start from scratch with Python fundamentals and work up to building your first neural network. No prior experience expected.
- Python programming basics
- Data manipulation with pandas
- Statistical concepts that matter
- First supervised learning models
Applied Track
You already code. Now learn how to make computers learn. Focus on practical implementations and real datasets.
- Classification and regression tasks
- Feature engineering techniques
- Model evaluation strategies
- Deployment considerations
Advanced Track
Deep learning architectures, custom model design, and optimization. For those ready to push boundaries.
- Neural network architectures
- Computer vision projects
- Natural language processing
- Research paper implementation
Performance That Counts
Numbers tell part of the story. Our students typically see measurable progress within the first three months, but that's just the start. The real transformation happens when you build something that solves an actual problem.
We track what matters. Not how fast you finish, but whether you can actually build models that perform well on unseen data. That's the benchmark that counts in the field.
Your Learning Journey
Diagnostic Assessment
We start by figuring out what you already know. Not a test you can fail, just an honest conversation about your background and a small coding exercise. Takes about 90 minutes and helps us recommend the right track.
Structured Modules
Each module runs 4-6 weeks. You'll work through concepts, implement them in code, then apply them to a mini-project. The pace is challenging but manageable if you can dedicate 12-15 hours weekly.
Capstone Development
The final three months focus on your portfolio piece. Pick a problem you care about, design a solution, build it, and document everything. This becomes the centerpiece of your portfolio when you're ready to look for opportunities.
Learn From Someone Who's Done It
Henrik Westerlund spent eight years building production ML systems before he started teaching. He's worked on recommendation engines, fraud detection models, and computer vision applications that process millions of transactions daily.
What makes Henrik different is his approach to teaching. He doesn't just show you how to run algorithms. He explains why certain approaches work in production and others fall apart when real data hits them.
- Led ML team at fintech startup through Series B
- Published research on model interpretability
- Guest lecturer at universities across Southeast Asia
- Active contributor to open-source ML tools
- Mentored over 200 developers since 2019
Henrik runs technical workshops twice monthly and holds open office hours every Friday. When you're stuck on a concept or debugging a model, you'll have direct access to someone who's solved these problems professionally.
Next Cohort Starts September 2025
We're accepting applications for our autumn program now. Spots are limited to maintain the mentorship quality we're known for. The application process includes a technical assessment and a conversation with our team.