Amazon Ml Summer School 2024 Registration, Date, Shortlisted Students

Amazon Ml Summer School 2024 Registration

The Amazon ML Summer School offers students a chance to acquire essential Machine Learning (ML) skills, laying the groundwork for a potential career in this field. This intensive program delves into core ML subjects, providing a valuable learning experience where participants can engage with experts from Amazon, including scientists from Australia and abroad who possess extensive expertise in various ML domains.

In a pioneering move for Australia, Amazon is set to introduce a four-week ML program scheduled for February or March 2024, and registrations are now open! Taking place on weekends from Saturday, February 24th to Sunday, March 17th, 2024, the Amazon ML Summer School is designed to equip students with essential Machine Learning (ML) skills, laying the foundation for a potential career in this field.

This intensive program will cover key ML topics such as Supervised Learning, Deep Neural Networks, Probabilistic Graphical Models, Dimensionality Reduction, and Unsupervised Learning. Participants will have the invaluable opportunity to learn from and engage with Amazon scientists who possess extensive expertise in their respective ML domains.

Commencing on June 19, 2024, the AI Programming with Python Nanodegree program starts.

Aims of Amazon ML Summer School :

1. Acquire comprehensive understanding of crucial ML subjects from Amazon Scientists.

2. Utilize this platform for engaging with Scientists and exploring cutting-edge innovations.

3. Seize the chance to equip oneself for a future career in machine learning.

AWS AI & ML Scholarship Event Dates :

Event	Start Date	End Date
Applications Open – Summer Cohort	1 February 2024	31 May 2024
Application Open – Winter Cohort	1 June 2024	30 September 2024
Udacity NanoDegree 1 – Summer Cohort	19 June 2024	18 October 2024
Udacity NanoDegree 1 – Winter Cohort	16 October 2024	14 February 2025
Udacity NanoDegree 2 – Combined Cohorts	5 May 2025	6 October 2025

Selection Criteria :

The selection criteria entail two parts :

• Part A, comprising 20 multiple-choice questions covering fundamental ML concepts and a math fundamentals test encompassing topics like probability, statistics, and linear algebra.
• Part B, featuring two programming questions.
• The total duration of the selection test will be 60 minutes.

There are no charges or fees linked to this program.

Eligibility For Enrolment :

• Eligibility for enrolment in ML Summer School includes :
• Being enrolled in Bachelor’s /Master’s / PhD programs in engineering at any recognised institute in Australia.
• Expected graduation years include 2024, 2025, or 2026.

How to Apply for AWS AI & ML Scholarship ?

• Sign in to AWS DeepRacer Student and opt in to the scholarship program.
• Fulfill prerequisites to access your exclusive application code.
• Utilize your unique code to submit your scholarship application on Udacity.

Prequalifying for the AWS AI & ML Scholarship Program  :

• Review coursework and pass assessments, attaining a minimum score of 80% on all required assessments within the AWS DeepRacer Student learning modules. These assessments correspond to various chapters available in the Learn section of the AWS DeepRacer Student navigation pane.
• Achieve a minimum single lap time of less than 2 minutes on any single leaderboard in the AWS DeepRacer Student League. A new leaderboard featuring a fresh track design launched each month from March to September.

Eligibility for the AWS AI & ML Scholarship :

Students eligible for the AWS AI & ML Scholarship program selected based on :

1. Successful completion of prerequisites within AWS DeepRacer Student.

2. Submission of their application on the Udacity website by the submission deadline.

3. Udacity utilises various application data, including prerequisite knowledge assessments, to determine 2,000 scholarship recipients annually.

4. The top 500 students for the advanced Udacity Nano degree program with one-on-one mentoring are selected based on : Completion of coursework during the first nano degree, including course assessments and module completion.

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