Description
Google Machine Learning Crash Course: AI Best Practices from the Source
Google uses machine learning in everything—from Search to Maps to Gmail. This course, created by Google’s AI team, teaches you the **practical strategies** that make ML work in the real world: framing problems, managing data, avoiding pitfalls, and deploying models at scale.
What You’ll Master
- AI-first thinking—how Google transformed from mobile-first to AI-first
- Two phases of ML—prototyping vs. production—and why they’re different
- Data strategy—why data quality beats algorithm choice
- Training/serving skew—the #1 cause of real-world model failure
- ML fairness and bias—evaluating models for inclusive outcomes
- Pre-trained APIs—use Vision, NLP, and Translation without training models
Real Google Examples
- How Google Photos recognizes “hugs” and “beaches”
- How Google Translate went from phrase-based to neural
- How YouTube uses recommendation systems responsibly
Why This Course?
Most courses teach ML in a vacuum. This one teaches **how ML works inside one of the world’s most advanced AI companies**.
Who Should Take This?
- Product managers scoping AI features
- Engineers transitioning into ML roles
- Startup founders building AI products
- Students preparing for ML system design interviews
From Theory to Google-Grade Practice
You’ll learn not just how to build models—but how to build models that last.
Ready to think like a Googler? Enroll now.
