Leading edutech platform Coursera has raised $130 million as part of the  Series F funding round. This financing would enable Coursera to bring quality and highly contextual learning material to aspiring professionals in the forward specializations during the ongoing COVID-19 pandemic.

Yesterday, NVIDIA and UF announced a strategic alliance to develop AI and Data Science education facilities for the State of Florida. The partnership includes $70 million joint funding towards building the world’s fastest AI Supercomputer for higher education.

Coursera’s latest $130 million funding further bolsters the rapid movement towards research and education-driven approach to sustain Data Science as a key specialization in the Industrial Revolution 4.0.

The latest funding brings the company’s cash balance to more than $300 million. The round was led by NEA and joined by existing investors Kleiner Perkins, SEEK Group, Learn Capital, SuRo Capital Corp, and G Squared.

Coursera, along with its community of partners, has been at the forefront of preparing individuals and institutions for the Fourth Industrial Revolution. The ongoing pandemic has further accelerated the rate of digital transformation among companies. The jobs of the future will be powered by a highly-skilled and distributed workforce, collaborating virtually to deliver value.

Coursera remains committed toward bringing on-demand educational content and assets to millions of learners, thousands of campuses and companies, and hundreds of governments. The company understands its role of staying well-resourced to serve the world at this particularly challenging time. It confirmed using the funds to double down on our product and engineering efforts, expand our job-relevant catalog, and further grow global presence.

Coursera has witnessed a huge demand for two of their COVID-focused initiatives —

1. Campus Response Initiative to help universities teach impacted students; and
2. Workforce Recovery Initiative to help governments reskill unemployed workers.

The Digital Skillset is Missing its Mark in the Data-Driven Economy

In a recent report, NTT Data has revealed the glaring holes in the talent pool pertaining to the data science industry. Different industry verticals are plagued by complex challenges, all stemming from the lack of analytical skillset among the professionals. Industry-driven learning, traning and development platforms are an important cog in this machinery, looking at the current and future data trends.

Mapping Your Digital Transformation Journey- Stages Of Digital Maturity

The world is facing unprecedented economic disruption, and the need to develop skills for a digital future is even more apparent now. Coursera and other edutech firms should continue to innovate and bring quality learning material to democratize AI education for all.

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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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