Published on : May 24, 2018
Technology companies are actively entering into collaborations with providers with education-focused companies to help budding developers and IT professionals get a feel of real-world models. The technology giant Google Inc. has recently in May 2018 teamed up with Coursera, a venture-backed technology company offering online courses, to unveil a machine learning (ML) specialization on Coursera using Google cloud platform. The specialization course module comprise five courses and enable students to delve deeper into ML models by taking advantage of end-to-end machine learning and cloud capability. The target audience can enroll in the specialization to learn the fundamentals of setting the environment, to create these datasets, increase the relevance of distributed models they make, and help themselves finding right parameters in which the models can work.
Course Expected to Gather Steam among Employers looking to skill their Workforces with ML Tech
The course is aimed at equipping students and IT professionals in companies access Google’s resources so that they can use it in the production cloud. The initiative is focused on helping employees augment their skills, against the background that machine learning is witnessing wider commercialization by players in the industry. The course is expected to gather steam among employers who want their workforce get real-life experimental exposure to machine learning technologies. This is likely to prove vital given the fact machine learning expertise ready for employment is few and far between.
Dedicated GPU and Advanced Hardware key Features of End-to-end ML Models
Google and Coursera believe the launching of the course will fill an industry gap by providing a dedicated graphics processing unit (GPU) and sophisticated hardware which makes such courses attractive. This is made possible with the Google cloud platform. As the Big Data and ML Technology head stated that these courses are different from many others as they will enable target audient to build day-to-day machine learning models rather than advanced ones, adding value to the already existing applications.