Master Machine Learning Through Real Projects
We teach machine learning by building actual systems that solve real problems. No theory-only courses here — you'll work with live datasets and deploy working models from day one.
Explore Programs
Why Our Approach Works
Most ML courses teach algorithms in isolation. We start with business problems and show you how to solve them. You'll understand not just how machine learning works, but when and why to use it.
Problem-First Learning
Every lesson begins with a real business challenge. You'll learn to ask the right questions before diving into technical solutions. This approach helps you think like a practitioner, not just a student.
Industry Dataset Experience
Work with messy, incomplete data from actual companies. You'll learn data cleaning, feature engineering, and validation techniques that textbooks never teach you.
End-to-End Implementation
Build models that actually get used. We cover deployment, monitoring, and maintenance — the parts that make the difference between a school project and professional work.
Comprehensive ML Program
Our nine-month program takes you from understanding basic concepts to deploying production systems. You'll work on three major projects that become part of your portfolio.
- Build recommendation systems using collaborative filtering and content analysis
- Create predictive models for time series forecasting and anomaly detection
- Develop computer vision applications for image classification and object detection
- Implement natural language processing for sentiment analysis and text generation
- Deploy models using cloud platforms and monitor their performance
Classes start in August 2025. We keep groups small — maximum 16 students per cohort — so everyone gets individual attention.
Our Three-Phase Learning Method
Each project follows the same proven structure. You'll repeat this cycle three times with increasing complexity, building confidence and expertise with each iteration.
Understand & Explore
Start by understanding the business context and exploring the data. You'll learn to ask good questions and identify patterns before building anything.
Build & Validate
Develop your model using appropriate algorithms and validation techniques. Focus on creating something that works reliably, not just achieving high accuracy scores.
Deploy & Monitor
Put your model into production and track its performance over time. Learn to handle edge cases and maintain systems as they encounter real-world data.
This methodology comes from our experience building ML systems for companies across Taiwan. We've seen what works in practice, not just in theory.
Learning That Sticks
You'll work in small teams on challenging problems. This collaborative approach mirrors how ML projects actually happen in companies — rarely does one person handle everything alone.
Our workshops focus on practical skills you can't learn from videos. Things like debugging models that seem to work in development but fail in production, or figuring out why your accuracy dropped after three months.
We also cover the business side of ML — how to communicate results to non-technical stakeholders and make recommendations that actually get implemented.
What Our Students Say
These are real stories from people who completed our program. They're not making millions or revolutionizing industries — they're doing solid, meaningful work with machine learning.
Liora Nakamura
Data Analyst at Tech Startup
Astrid Johansson
ML Engineer at Manufacturing CompanyReady to Start Building?
Our next cohort begins August 2025. Applications open in April. Get details about curriculum, schedule, and requirements.