Amazon India has launched the second version of its Machine Learning (ML) Summer School; a program that aims to provide students with the opportunity to learn key ML technologies from scientists at Amazon, making them industry-ready for careers in science.
What is the duration and main topics to be covered in the course
The course, conducted over four weekends in July, will provide students with the opportunity to gain skills on key ML topics, including: Supervised Learning, Deep Neural Networks, Modeling Sequential Modeling, Dimensional Reduction, Unsupervised Learning and two new modules, Reinforcement Learning and Inferential Causality. Participants will also have access to the Amazon Research Days (ARD) conference – an engagement program held each November. ARD connects the scientific community at Amazon, industry leaders, and academic researchers in the field of AI around the world.
Amazon will also run the ML Challenge, their flagship ML contest in August, which is an opportunity for students to work on Amazon data sets, generate new ideas, and build innovative solutions for a problem statement in the real world. The winning teams will receive pre-positioning interviews (PPIs) for ML roles at Amazon along with cash prizes, swag, and certificates.
How to sign up for Amazon Summer school about machine learning
Summer School ML open to students in their final or final year of an integrated Bachelor/Master/Master/PhD degree registered at any campus in India. Eligible students will be required to take an online assessment that focuses on fundamental ML concepts and math fundamentals on topics such as probability, statistics, and linear algebra. The top 3000 students then enroll in the ML Summer School – who participate in 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 take part in the Amazon ML Challenge, for hands-on practical application of skills and knowledge learned through modules to solve a real-world problem, according to the company.