Three new machine learning courses

Many years ago, I took a dance lesson in Budapest to learn the csárdás, a Hungarian folk dance. The instructor shouted directions to me in enthusiastic Hungarian, a language I didn't understand, yet I still learned the dance by mimicking the instructor and the expert students. Now, I do love clear directions in a lesson—I am a technical writer, after all—but it’s remarkable what a person can learn by emulating the experts.  

In fact, you can learn a lot about machine learning by emulating the experts. That’s why we’ve teamed with ML experts to create online courses to help researchers, developers, and students. Here are three new courses:

• Clustering: Introduces clustering techniques, which help find patterns and related groups in complex data. This course focuses on k-means, which is the most popular clustering algorithm. Although k-means is relatively easy to understand, defining similarity measures for k-means is challenging and fascinating.
• Recommendation Systems: Teaches you how to create ML models that suggest relevant content to users, leveraging the experiences of Google's recommendation system experts. You'll discover both content-based and collaborative filtering, and uncover the mathematical alchemy of matrix factorization. To get the most out of this course, you'll need at least a little background in linear algebra.
• Testing and Debugging: Explains the tricks that Google's ML experts use to test and debug ML models. Google's ML experts have spent thousands of hours deciphering the signals that faulty ML models emit. Learn from their mistakes.    

These new courses are engaging, practical, and helpful. They build on a series of courses we released last year, starting with Machine Learning Course Crash (MLCC), which teaches the fundamentals of ML. If you enjoyed MLCC, you're ready for these new courses. They will push you to think differently about the way you approach your work. Take these courses to copy the moves of the world's best ML experts.