Google and Coursera launch a new machine learning specialization

Frederic Lardinois
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Over the last few years, Google and Coursera have regularly teamed up to launch a number of online courses for developers and IT pros. Among those was the Machine Learning Crash course, which provides developers with an introduction to machine learning. Now, building on that, the two companies are launching a machine learning specialization on Coursera. This new specialization, which consists of five courses, has an even more practical focus.The new specialization, called  “Machine Learning with TensorFlow on Google Cloud Platform,” has students build real-world machine learning models. It takes them from setting up their environment to learning how to create and sanitize datasets to writing distributed models in TensorFlow, improving the accuracy of those models and tuning them to find the right parameters.

As Google’s Big Data and Machine Learning Tech Lead Lak Lakshmanan told me, his team heard that students and companies really liked the original machine learning course but wanted an option to dig deeper into the material. Students wanted to know not just how to build a basic model but also how to then use it in production in the cloud, for example, or how to build the data pipeline for it and figure out how to tune the parameters to get better results.

Leah Belsky, Coursera’s  VP of enterprise development, echoed this and noted that this kind of specialization with a focus on practical models will make the credential more meaningful for employers.

The target audience for the specialization is somebody who wants to build new skills — and that’s pretty much every developers, especially now that machine learning is making inroads in virtually every area of tech. And since it’s almost impossible to hire machine learning experts, this course will surely be attractive to many employers who want their existing workforce to gain these skills.

As Lakshmanan noted, there are plenty of use cases for leading-edge kind of machine learning models, but what these courses focus on are more of the “day-to-day models” that can bring additional value to many existing products. Because of the focus on real-world problems, Lakshmanan also noted that the course should be useful for newly minted graduates who may be more familiar with the theories of machine learning than building products.

He also noted that only a few years ago, getting started with a course like this would have been rather cumbersome, not in the least because you need relatively powerful hardware with a dedicated GPU to work productively. Now, however, thanks to the various cloud platforms that offer GPU access or even specialized hardware like Google Cloud’s TPUs, the barrier of entry has dropped significantly.

It’s worth noting that these courses expect that you are already a somewhat competent programmer. While it has gotten much easier to start with machine learning thanks to new frameworks like TensorFlow, this is still an advanced skill. It’ll surely still be a while before we see a “get started with programming in Python by building a machine learning model” course.

Looking ahead, Lakshmanan also noted that the team is looking at a next course that would build upon the existing one, but with a focus on working with unstructured data. That’s a different class of problem with its own skill set and one that’ll allow the graduates of the first course to apply their knowledge to a whole different set of data.


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