Amazon India announces second edition of Machine Learning Summer School: How to apply and other details

Amazon India has launched the second edition of Machine Learning (ML) Summer School; a 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.
What is the duration and key topics to be covered in the course
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 an 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.
How to apply for Amazon Machine Learning Summer School
ML Summer School is open to students in the penultimate or final year of their Bachelor’s/Master’s/Integrated Masters/PhD degree enrolled in any campus in India. Eligible students will be required to take an online assessment focused on basic ML concepts and math fundamentals on topics such as probability, statistics and linear algebra. The top 3000 students are then enrolled to ML Summer School – who attend eight virtual classroom sessions over four weekends, each session followed by live Q&A sessions with scientists at Amazon. ML Summer School participants can also participate in Amazon ML Challenge, to practise hands-on application of skills and learnings acquired via modules to solve a real-world problem statement, as per the company.