Amazon India introduces machine learning summer school
A batch of students from select tech campuses in India will be presented with the opportunity to engage through virtual classroom tutorials followed by interactive Q&A sessions with scientists at Amazon.
Bengaluru: Amazon India on Sunday announced the launch of ML Summer School which will provide an integrated learning experience for students to gain applied Machine Learning (ML) skills.
A batch of students from select tech campuses in India will be presented with the opportunity to engage through virtual classroom tutorials followed by interactive Q&A sessions with scientists at Amazon.
For students with prior exposure to certain areas of ML, the programme can act as a refresher course, while additionally providing a practical perspective on ML applications in industry, the company said in a statement.
“With the pace of advancements in ML, we are proactively helping students to learn about the latest trends in the field of ML and apply them to solve real-world problems,” said Rajeev Rastogi, VP, India Machine Learning at Amazon.
“Our aim is to prepare students for science roles — this will help to reduce the gap between the growing demand for ML roles across companies and the talent pool with applied ML skills,” he added.
ML Summer School is open to engineering students in their pre-final/final year of Bachelors, Masters or PhD studies across select tech campuses in 2021.
Participants of ML Summer School will be identified through an online assessment.
They will also have access to the Amazon Research Days (ARD) conference where they can learn about technology trends in industry through presentations from renowned ML leaders around the world.
The curriculum of ML Summer School will cover the fundamental concepts in ML while linking them to practical industry applications through an immersive three-day course.
Students will get to learn first-hand on how advanced ML techniques such as Deep Learning and Probabilistic Graphical Models can be leveraged to solve specific business problems in the e-commerce domain such as demand forecasting, catalogue quality, product recommendations, search ranking and online advertising.
