Amazon’s Free Machine Learning Course for College Students: Learn the Skills You Need to Land Your Dream Job


Amazon’s Free Machine Learning Course for College Students: Learn the Skills You Need to Land Your Dream Job

Machine learning is one of the most in-demand skills in the tech industry today. Companies are increasingly looking for employees who can use machine learning to solve real-world problems.


If you’re a college student interested in learning machine learning, Amazon has a great opportunity for you. Amazon is offering a free machine learning course for college students. The course is called “Machine Learning Foundations” and it is offered through Udacity.

The course covers the basics of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. It also teaches you how to use AWS services for machine learning, such as Amazon SageMaker.

The course is self-paced, so you can study at your own pace. It can be completed in 8 weeks.

Last year, Amazon announced the second edition of its India Machine Learning (ML) Summer School. Today, in a furtherance of the effort to help students in India prepare for industry jobs in machine learning, Amazon announced registrations are open for the third edition of the ML Summer School through Sept. 6.

What You’ll Learn

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 the successful second edition in 2022,  the third edition of Amazon ML Summer School is being launched for students enrolled in any recognized institute in India and who are expected to graduate in 2024 or 2025.

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 2024 or 2025 are eligible to enroll in ML Summer School. 

Here are some of the benefits of taking Amazon’s free machine learning course:

  • Learn from experts: The course is taught by experts from Amazon who have real-world experience in machine learning.
  • Get hands-on experience: The course includes hands-on exercises that will help you apply what you learn.
  • Build your portfolio: The course projects can be added to your portfolio to show potential employers your skills.
  • Network with other learners: The course forum is a great place to connect with other machine learning learners and share ideas.
  • Get certified: Upon completion of the course, you will receive a certificate from Udacity.

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 two Programming questions. The overall test duration will be 60 minutes.

Is there a registration fee or course fee?

No, there are no fees associated with this program.

How to Apply?

Here are the steps on how to apply for Amazon’s free ML School course:

  1. Go to the Amazon ML School website:
  2. Click on the “Apply Now” button.
  3. Fill out the application form.
  4. Upload your resume and a letter of intent.
  5. Take the selection test.

The selection test will be held on September 9, 2023. The test will cover math, programming, and basic machine learning concepts.

The deadline to apply is September 6, 2023.

Here are some additional things to keep in mind when applying for the course:

  • You must be an engineering student enrolled in a bachelor’s, master’s, or PhD program at a recognized institution in India.
  • You must be expected to graduate in 2024 or 2025.
  • You must have a strong background in mathematics and computer science.
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