AI INTERNSHIP WITH AMAZON WEB SERVICES (AWS)

Starts: December 2024

Accepting Grade 8-12 Students

Book Consultation

About the Internship

No idea about AI? No problem!

No coding knowledge? No problem!

 

AI Internship with Amazon (AWS) combines LIVE learning and LIVE internship. It is a 4 Weeks long programme designed for high school students in grades 8 to 12 to understand and apply the fundamental concepts of AI. Students understand the logic and math behind different models that enable chatbots, self-driving cars, image recognition and much more.

 

They are taught, guided and assessed by industry mentors from Amazon (AWS).

 

Students also take up a social challenge and combine their problem-solving and AI knowledge to create a solution. At the end of the programme, they are assessed and certified by Amazon (AWS) for this project.

 

No Coding knowledge or computing background is required!

 

 

At a Glance
Program Duration : 4 Weeks
Internship Fee : USD 399
Programme Outcome : Certification from Amazon Web Services (AWS)

 

 

Register Now 

Download Factsheet

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The AIYA benefit

Learning Intervention

 
Sessions Concepts Hands-On Project Assessment
1 Introduction to Artificial Intelligence
  • Understanding patterns, training and learning
  • Introduction to intelligence and Artificial Intelligence (AI)
  • Applications of AI
  • AI testing (Turing test, Chinese room argument testing, etc)
  • AI ethics
  • Project Briefing & Group Formation
  • Introduction and briefing on Amazon tools (SageMaker & Lex)
  • Student account sign-up
  • Project Progress Update and Review in every session
  • Quiz after every session
2 Introduction to Natural Language Processing (NLP)
  • Importance of data and introduction to BigData
  • Introduction to probability and Bayesian Classifier
  • Understanding the natural language, grammars (POS tagging), etc.
  • Applications of Natural Language Processing
  • Chatbot concepts like intent, utterance, actions, slot, etc.
  • Demo of Chatbot via Amazon Lex and deploying on Slack
  • Setting up SageMaker and Jupyter Notebook instance
3 Introduction to Deep Learning and Artificial Neural Networks (ANN)
  • Introduction to Perceptron
  • Build first image classifier
  • Introduction to Multilevel Perceptron (MLP) classifier
  • Architecture of Neural Network
  • Introduction to Convolution Neural Network (CNN)
  • Object detection via Amazon studio maker
  • Chatbot project review from the previous session
4 Image Classification
  • Basic concepts on image classification
  • Preparing and pre-processing with MNIST (Modified National Institute of Standards and Technology database) data set
  • Handwritten Digit Classification using MNIST dataset
  • Image Classification
5 Deployment and Testing
  • Importance of model deployment and testing
  • Techniques for deploying models using SageMaker
  • Testing models for accuracy and performance
  • Deploying and testing models using SageMaker
 
Sessions Concepts Hands-On Project Assessment
1 Introduction to Artificial Intelligence
  • Understanding patterns, training and learning
  • Introduction to intelligence and Artificial Intelligence (AI)
  • Applications of AI
  • AI testing (Turing test, Chinese room argument testing, etc)
  • AI ethics
  • Project Briefing & Group Formation
  • Introduction and briefing on Amazon tools (SageMaker & Lex)
  • Student account sign-up
  • Project Progress Update and Review in every session
  • Quiz after every session
2 Introduction to Natural Language Processing (NLP)
  • Importance of data and introduction to BigData
  • Introduction to probability and Bayesian Classifier
  • Understanding the natural language, grammars (POS tagging), etc.
  • Applications of Natural Language Processing
  • Chatbot concepts like intent, utterance, actions, slot, etc.
  • Demo of Chatbot via Amazon Lex and deploying on Slack
  • Setting up SageMaker and Jupyter Notebook instance
3 Introduction to Deep Learning and Artificial Neural Networks (ANN)
  • Introduction to Perceptron
  • Build first image classifier
  • Introduction to Multilevel Perceptron (MLP) classifier
  • Architecture of Neural Network
  • Introduction to Convolution Neural Network (CNN)
  • Object detection via Amazon studio maker
  • Chatbot project review from the previous session
4 Image Classification
  • Basic concepts on image classification
  • Preparing and pre-processing with MNIST (Modified National Institute of Standards and Technology database) data set
  • Handwritten Digit Classification using MNIST dataset
  • Image Classification
5 Deployment and Testing
  • Importance of model deployment and testing
  • Techniques for deploying models using SageMaker
  • Testing models for accuracy and performance
  • Deploying and testing models using SageMaker
6 Capstone Project
  • Final Session
 Capstone Project Presentation, Assessment & Evaluation by Amazon (AWS)  

Assessment Criteria

 

Amazon Assessment Component Weightage
Continuous assessment (MCQ Quizzes) 20%
End Term assessment (MCQ) 30%            
Project Assessment 50%

 

1. Certificate of Completion from Amazon (AWS)

 

 
 
testimonial

DISCLAIMER: ABOVE DOCUMENT IS INDICATIVE AND SUBJECT TO MINOR CHANGES BASED ON PARTNER DISCRETION.

 

Certificate of Completion Amazon
Pedagogy
Learning Goals
 
Sessions Concepts Hands-On Project Assessment
1 Introduction to Artificial Intelligence
  • Understanding patterns, training and learning
  • Introduction to intelligence and Artificial Intelligence (AI)
  • Applications of AI
  • AI testing (Turing test, Chinese room argument testing, etc)
  • AI ethics
  • Project Briefing & Group Formation
  • Introduction and briefing on Amazon tools (SageMaker & Lex)
  • Student account sign-up
  • Project Progress Update and Review in every session
  • Quiz after every session
2 Introduction to Natural Language Processing (NLP)
  • Importance of data and introduction to BigData
  • Introduction to probability and Bayesian Classifier
  • Understanding the natural language, grammars (POS tagging), etc.
  • Applications of Natural Language Processing
  • Chatbot concepts like intent, utterance, actions, slot, etc.
  • Demo of Chatbot via Amazon Lex and deploying on Slack
  • Setting up SageMaker and Jupyter Notebook instance
3 Introduction to Deep Learning and Artificial Neural Networks (ANN)
  • Introduction to Perceptron
  • Build first image classifier
  • Introduction to Multilevel Perceptron (MLP) classifier
  • Architecture of Neural Network
  • Introduction to Convolution Neural Network (CNN)
  • Object detection via Amazon studio maker
  • Chatbot project review from the previous session
4 Image Classification
  • Basic concepts on image classification
  • Preparing and pre-processing with MNIST (Modified National Institute of Standards and Technology database) data set
  • Handwritten Digit Classification using MNIST dataset
  • Image Classification
5 Deployment and Testing
  • Importance of model deployment and testing
  • Techniques for deploying models using SageMaker
  • Testing models for accuracy and performance
  • Deploying and testing models using SageMaker
Schedule
 
Sessions Concepts Hands-On Project Assessment
1 Introduction to Artificial Intelligence
  • Understanding patterns, training and learning
  • Introduction to intelligence and Artificial Intelligence (AI)
  • Applications of AI
  • AI testing (Turing test, Chinese room argument testing, etc)
  • AI ethics
  • Project Briefing & Group Formation
  • Introduction and briefing on Amazon tools (SageMaker & Lex)
  • Student account sign-up
  • Project Progress Update and Review in every session
  • Quiz after every session
2 Introduction to Natural Language Processing (NLP)
  • Importance of data and introduction to BigData
  • Introduction to probability and Bayesian Classifier
  • Understanding the natural language, grammars (POS tagging), etc.
  • Applications of Natural Language Processing
  • Chatbot concepts like intent, utterance, actions, slot, etc.
  • Demo of Chatbot via Amazon Lex and deploying on Slack
  • Setting up SageMaker and Jupyter Notebook instance
3 Introduction to Deep Learning and Artificial Neural Networks (ANN)
  • Introduction to Perceptron
  • Build first image classifier
  • Introduction to Multilevel Perceptron (MLP) classifier
  • Architecture of Neural Network
  • Introduction to Convolution Neural Network (CNN)
  • Object detection via Amazon studio maker
  • Chatbot project review from the previous session
4 Image Classification
  • Basic concepts on image classification
  • Preparing and pre-processing with MNIST (Modified National Institute of Standards and Technology database) data set
  • Handwritten Digit Classification using MNIST dataset
  • Image Classification
5 Deployment and Testing
  • Importance of model deployment and testing
  • Techniques for deploying models using SageMaker
  • Testing models for accuracy and performance
  • Deploying and testing models using SageMaker
6 Capstone Project
  • Final Session
 Capstone Project Presentation, Assessment & Evaluation by Amazon (AWS)  
Assessment

Assessment Criteria

 

Amazon Assessment Component Weightage
Continuous assessment (MCQ Quizzes) 20%
End Term assessment (MCQ) 30%            
Project Assessment 50%
Programme Completion Documents

 

1. Certificate of Completion from Amazon (AWS)

 

 
 
testimonial

DISCLAIMER: ABOVE DOCUMENT IS INDICATIVE AND SUBJECT TO MINOR CHANGES BASED ON PARTNER DISCRETION.

 

Certificate of Completion Amazon

Admissions

Associates

Amazon  

Amazon - World leader in e-commerce, cloud computing, digital streaming and AI.
 

Amazon Web Services, Inc. (AWS) is a subsidiary of Amazon that provides on-demand cloud computing platforms and APIs to individuals, companies, and governments, on a metered pay-as-you-go basis. These cloud computing web services provide distributed computing processing capacity and software tools via AWS server farms

Amazon SageMaker is built on Amazon’s two decades of experience developing real-world ML applications, including product recommendations, personalization, intelligent shopping, robotics, and 
voice-assisted devices.
 

Amazon SageMaker is a fully managed machine learning service. With SageMaker, data scientists and developers can quickly and easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment.

Amazon  

Amazon - World leader in e-commerce, cloud computing, digital streaming and AI.
 

Amazon Web Services, Inc. (AWS) is a subsidiary of Amazon that provides on-demand cloud computing platforms and APIs to individuals, companies, and governments, on a metered pay-as-you-go basis. These cloud computing web services provide distributed computing processing capacity and software tools via AWS server farms

Amazon SageMaker is built on Amazon’s two decades of experience developing real-world ML applications, including product recommendations, personalization, intelligent shopping, robotics, and 
voice-assisted devices.
 

Amazon SageMaker is a fully managed machine learning service. With SageMaker, data scientists and developers can quickly and easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment.

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Frequently Asked Questions

What is AI Internship with Amazon (AWS)?

AI Internship with Amazon combines LIVE learning and LIVE internship. It is a 4 weeks long programme designed for high school students from grades 8 to 12 to understand and apply the fundamental concepts of AI. Students understand the logic and math behind different models that enable chatbots, self-driving cars, image recognition and much more.

The academic internship is conducted by Corporate Gurukul Pte. Ltd., Singapore in association and certified by:

  • Amazon (AWS)

It is a LIVE online academic internship conducted via Zoom Video Communication

It is an academic internship, which is a hands-on guided experience in an academic environment to hone industry-relevant skills and knowledge in a chosen domain with mentorship and training by industry professional(s).

  • It is a no code internship
  • Students understand the logic that powers the complex AI algorithms instead of straight away diving into code.
  • The industry AI frontiers are from Amazon Web Services (AWS). They have at least 5+ years of experience in AI teaching and research.
  • Students get certified by Amazon Web Services (AWS).
  • AI Internship with Amazon is a popular choice for students from India, Middle East and Southeast Asia. Students collaborate in teams with a global audience.

How do I apply?

The process of application is given in the Fact sheet which you can download via the website. Upon filling the application form on website, one of our counsellors will be in touch with you to take it forward and assist you with the enrollment. You may also enrol through our website and make the payment via a Debit/Credit card or Online Banking.

This programme is open to all students from grade 8-12. For further details check the 'Admissions' section on this webpage.

No, our course is specially designed to ensure that students from all streams and areas of study can easily understand the concepts taught at this internship.

Yes, students studying in the ICSE, CBSE, IB MYP, IB DP and IGCSE boards can apply for the programme. Students can be of any nationality, ethnicity and belong to any country!

The schedule, date and time are subject to change based on the availability of industry professionals from Amazon Web Services (AWS). Prior notice will be given to all concerned parties on the change of date and the best efforts will be made by Corporate Gurukul to accommodate all parties when deciding the revised schedule, date and time.

Is it a selection-based programme?

Yes

All applications are scrutinized on the basis of:

  1. Overall academic performance (grades) in current year or previous year
  2. Career Goals – Should be aligned to solving business or social challenges problems using AI

What is the last date to pay for the registration fees?

Please refer the Offer Letter issued to you.

  • Curriculum design, training and assignments by certified professional(s) from Amazon Web Services (AWS)
  • ‘Certificate of Completion’ by Amazon Web Services (AWS)

Which certificate(s) and transcript(s) will I get after completion of this academic internship?

Please refer to the Programme Completion Documents section above.

Issuance of certificates will be at Corporate Gurukul’s discretion subject to:

  1. the participant’s performance
  2. completion of assignments and
  3. 90% attendance during the academic internship

The e-certificates will be issued to you after 45 working days post-completion of the internship.

The hands-on sessions are conducted Online via Zoom Video Communication

Laptop with the specifications mentioned in ACADEMIC INTERNSHIP PREREQUISITES is MANDATORY for all participants.

No. The entire academic internship is delivered online.

Yes, you will be assigned teams for project work and assignments. These teams typically consist of 4-5 members.

No, the team members are assigned by Corporate Gurukul team in consultation with the industry expert(s). They are created keeping in mind that the prerequisite readiness and a proper mix of students from various institutions. Any request for choice of team member will not be entertained.

All assignments and projects will be submitted on the Learning Management System, Acadly.

Assignments will be given after every session and need to be submitted as per specified deadline. You may be required to work on the assignments in teams or individually as per the nature of assignment.

You are required to complete one project during the duration of the programme. This will be done in teams.

The project presentations will be conducted post Amazon sessions.

Yes, Amazon sessions will be graded.

The MCQ Quiz will be conducted during the session.

Any late submissions of project, assignments or quizzes beyond specified deadline will carry negative marks.

About AI Internship with Amazon (AWS)

What is AI Internship with Amazon (AWS)?

AI Internship with Amazon combines LIVE learning and LIVE internship. It is a 4 weeks long programme designed for high school students from grades 8 to 12 to understand and apply the fundamental concepts of AI. Students understand the logic and math behind different models that enable chatbots, self-driving cars, image recognition and much more.

The academic internship is conducted by Corporate Gurukul Pte. Ltd., Singapore in association and certified by:

  • Amazon (AWS)

It is a LIVE online academic internship conducted via Zoom Video Communication

It is an academic internship, which is a hands-on guided experience in an academic environment to hone industry-relevant skills and knowledge in a chosen domain with mentorship and training by industry professional(s).

  • It is a no code internship
  • Students understand the logic that powers the complex AI algorithms instead of straight away diving into code.
  • The industry AI frontiers are from Amazon Web Services (AWS). They have at least 5+ years of experience in AI teaching and research.
  • Students get certified by Amazon Web Services (AWS).
  • AI Internship with Amazon is a popular choice for students from India, Middle East and Southeast Asia. Students collaborate in teams with a global audience.

Applying for AI Internship with Amazon (AWS)

How do I apply?

The process of application is given in the Fact sheet which you can download via the website. Upon filling the application form on website, one of our counsellors will be in touch with you to take it forward and assist you with the enrollment. You may also enrol through our website and make the payment via a Debit/Credit card or Online Banking.

This programme is open to all students from grade 8-12. For further details check the 'Admissions' section on this webpage.

No, our course is specially designed to ensure that students from all streams and areas of study can easily understand the concepts taught at this internship.

Yes, students studying in the ICSE, CBSE, IB MYP, IB DP and IGCSE boards can apply for the programme. Students can be of any nationality, ethnicity and belong to any country!

The schedule, date and time are subject to change based on the availability of industry professionals from Amazon Web Services (AWS). Prior notice will be given to all concerned parties on the change of date and the best efforts will be made by Corporate Gurukul to accommodate all parties when deciding the revised schedule, date and time.

Academics

Is it a selection-based programme?

Yes

All applications are scrutinized on the basis of:

  1. Overall academic performance (grades) in current year or previous year
  2. Career Goals – Should be aligned to solving business or social challenges problems using AI

Payment

What is the last date to pay for the registration fees?

Please refer the Offer Letter issued to you.

  • Curriculum design, training and assignments by certified professional(s) from Amazon Web Services (AWS)
  • ‘Certificate of Completion’ by Amazon Web Services (AWS)

Programme Completion Documents

Which certificate(s) and transcript(s) will I get after completion of this academic internship?

Please refer to the Programme Completion Documents section above.

Issuance of certificates will be at Corporate Gurukul’s discretion subject to:

  1. the participant’s performance
  2. completion of assignments and
  3. 90% attendance during the academic internship

The e-certificates will be issued to you after 45 working days post-completion of the internship.

The hands-on sessions are conducted Online via Zoom Video Communication

Laptop with the specifications mentioned in ACADEMIC INTERNSHIP PREREQUISITES is MANDATORY for all participants.

No. The entire academic internship is delivered online.

Yes, you will be assigned teams for project work and assignments. These teams typically consist of 4-5 members.

No, the team members are assigned by Corporate Gurukul team in consultation with the industry expert(s). They are created keeping in mind that the prerequisite readiness and a proper mix of students from various institutions. Any request for choice of team member will not be entertained.

All assignments and projects will be submitted on the Learning Management System, Acadly.

Assignments will be given after every session and need to be submitted as per specified deadline. You may be required to work on the assignments in teams or individually as per the nature of assignment.

You are required to complete one project during the duration of the programme. This will be done in teams.

The project presentations will be conducted post Amazon sessions.

Yes, Amazon sessions will be graded.

The MCQ Quiz will be conducted during the session.

Any late submissions of project, assignments or quizzes beyond specified deadline will carry negative marks.

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