Amazon ML Summer School India
admin | 2022-06-13Click here to join Our whatsapp group
What is the eligibility criteria for enrolling in ML Summer School?
Engineering students enrolled in Bachelor’s/Master’s/ PhD degree from any recognized institute of India and are expected to graduate in 2023 or 2024 are eligible to enroll in ML Summer School.
What is the selection criteria?
The selection test will have two parts – Part A will consist of 20 MCQ on basic ML concepts and math fundamentals on topics such as probability, statistics and linear algebra. Part B will consist of one Programming question. The overall test duration will be 60 minutes.
Are Amazon’s past, current and incoming interns eligible to participate in this program?
Yes, Amazon’s past, current and incoming interns are eligible to participate in this program.
What is the program schedule for ML Summer School?
The program will be spread over eight days July 2, 3, 9, 10, 16, 17, 23, 24, 2022. Detailed program structure will be shared with selected students.
In which mode is the program being hosted in 2022?
Amazon ML Summer School will be held virtually in 2022.
Is there a registration fee or course fee?
No, there are no fees associated with this program.
Amazon ML Summer School aims at providing students the opportunity to gain Machine Learning (ML) skills which is the first step in becoming ready to build a career in ML. This program is an intensive course on key ML topics like Supervised Learning, Deep Neural Networks, Probabilistic Graphical Models, Dimensionality Reduction and Unsupervised Learning.
This is a great opportunity to learn from and interact with Scientists at Amazon who have immense knowledge in their ML domain. After a successful pilot in 2021, Amazon ML Summer School is being expanded and is being opened for students enrolled in any recognized institute in India who are expected to graduate in 2023 or 2024.
Key takeaways:
In-depth knowledge on key ML topics from Amazon Scientists
Platform to interact with Scientists and learn about break-through innovation
Opportunity to prepare for career in ML
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