Amazon India has launched the second edition of Machine Learning (ML) Summer School; an immersive program that aims to provide students the opportunity to learn key ML technologies from Scientists at Amazon making them industry ready for careers in science. The course, conducted over four weekends in July, will provide students with an opportunity to gain skills on key ML topics, including: Supervised Learning, Deep Neural Networks, Sequential Models, Dimensionality Reduction, Unsupervised Learning and two new modules, Reinforcement Learning and Causal Inference. Participants will also have access to Amazon Research Days (ARD) conference – an engagement program held in November every year.
ARD connects the scientific community at Amazon, industry leaders, and academic researchers in the field of AI around the world. Amazon will also conduct ML Challenge, its flagship ML competition in August, which is a unique opportunity for students to work on an Amazon dataset, bring in fresh ideas and build innovative solutions for a real-world problem statement. Winning teams will receive pre-placement interviews (PPIs) for ML roles at Amazon along with cash prizes, swag, and certificates.
Rajeev Rastogi, Vice President, International Machine Learning, Amazon says, “Amazon ML Summer School aims to provide participating students with best-in-class training on a broad range of topics which are at the core of modern Machine Learning, from fundamentals to state-of-the-art. The tutorial sessions covering the right mix of theoretical and practical knowledge will be delivered by our ML scientists who are experts in their field. This program will be a platform to help foster ML excellence and strive towards developing applied science skills in young talent. Our aim with the ML Summer School is to equip students with necessary practical experience and prepare them for science roles ahead.”
Debarshi Chanda, IIT Guwahati (one of the winning team members of the challenge), shares, “It was a great learning experience for me. Dealing with so much real-world data was unusual for us but we eventually came up with a strategy to make use of the entire training data. Learnt a lot of things, tried a lot of things. It will be a memory to cherish.”
