West indian countries

EXCLUSIVE: Amazon may expand ML Summer School to neighboring countries

To improve the skills of machine learning (ML) talents in the country, Amazon piloted the ML summer school for students from Indian universities last year. The e-commerce giant is in the second edition of the ML summer school which will focus on different ML topics with supervised learning, unsupervised learning, probabilistic graphical models, deep learning and more . Unique to India, Amazon can roll out this ML summer school to neighboring countries, where the time zones are not much different, Rajeev Rastogi, VP – International Machine Learning at Amazon told Business Today.

“This ML Summer School has evolved from a similar in-house ‘ML Gurukul’ program that we’ve been running for a few years to upskill Amazonians internally. The idea is to prepare thousands of ML students each year. industry to scientific roles. This ML Summer School covers a mix of theoretical and practical knowledge of industry. We seek to train and prepare students so that they can solve practical problems in industry. Multiple organizations within Amazon are involved in rolling it out – AWS and also the retail side of the business,” Rastogi says.

In the first year of our ML Summer School for 2021, Amazon selected 317 students from 20 selected Indian institutes to participate in this skills development program. The company has approached the Indian Institute of Technology (IIT) at its campuses in Bombay, Madras, Kharagpur, Delhi and Kanpur, as well as several other leading institutes, such as the Indian Institute of Science in Bengaluru and many others .

“For the second year, we have opened our ML Summer School to students in their penultimate or final year of their Bachelor’s/Master’s/Integrated Master’s/PhD enrolled at any campus in India. The top 3,000 engineering students will be enrolled in the ML Summer School – who will attend eight virtual classroom sessions over four weekends in July,” adds Rastogi.

Last year’s module included supervised learning, deep neural networks, probabilistic graphical models, dimensionality reduction and unsupervised learning. However, this year two new modules – Reinforcement Learning and Causal Inference – have been added.

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