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AI for Industry™

AI for Industry (AI4I)™ is a fully online programme to help learners gain proficiency in python and be able to program basic AI and data applications. The programme is hosted on the AI Makerspace online platform.  DataCamp  is used as a resource to support the learning required to complete the programme. Register any time and start learning!

Programme Structure

This programme consist of 2 parts:
1. Literacy in AI
2. Foundation in AI

Upon completion of each part, you will receive a certificate of completion.

AI4I – Literacy in AI

Literacy in AI is a 5-hour course which introduces modern AI technologies and applications so that you can be savvy consumers of AI products and services. You will learn how to identify opportunities and potential use cases in your work and daily lives, and build a simple AI model with online tools.
**AI4I – Literacy in AI is a prerequisite of AI4I – Foundations in AI. A learner will have to complete this module before you can start with AI4I – Foundations in AI.

AI4I – Foundations in AI

Foundation in AI is a 140 hours course which introduces to the technically inclined individuals to understand and use AI appropriately and be able to program basic AI and data applications in Python.

 

Programme Objectives

This programme aims to introduce AI to the learners and impart programming skills to allow learners to build basic AI models and applications.

Programme Flow

Curriculum

Literacy in AI

AI4I – Literacy in AI consists of a series of basic lessons consisting primarily of short videos, quizzes and examples for an introduction to AI.


Lesson Descriptions

Lesson 1: What is AI? (Part 1)
Lesson 2: What is AI? (Part 2)
Lesson 3: Why AI now?
Lesson 4: Getting to AI (Part 1)
Lesson 5: Getting to AI (Part 2)
Lesson 6: What AI cannot do
Lesson 7: Build an AI System
Lesson 8: AI, Jobs and You
Lesson 9: Modern AI Apps
Lesson 10: Are You Ready for AI?
Lesson 11: AI4I-Basic Practice
Lesson 12: AI4I-Basic Additional Practice

Foundations in AI

The python programming language is one of the most popular languages that data professionals use to do data analytics and AI. In this section, you will practice Python basics which act as a foundation for the rest of the sections in this course.

Lesson Descriptions
Lesson 1: Introduction to Python (DataCamp)
Lesson 2: Python Data Science Toolbox (Part 1) (DataCamp)
Lesson 3: Python Data Science Toolbox (Part 2) (DataCamp)

What makes Python so powerful is that people like yourselves have written ‘libraries’ or toolkits that condense and optimize complicated programming methods to simple commands for us to use quickly and easily. Learning how to use libraries is a key skill in becoming a proficient data professional.

Lesson Descriptions
Lesson 4: Intermediate Python (DataCamp)
Lesson 5: Cleaning Data in Python (DataCamp)
Lesson 6: pandas Foundations (DataCamp)
Lesson 7: Manipulating DataFrames with pandas (DataCamp)
Lesson 8: Merging DataFrames with pandas (DataCamp)

The first order of business whenever you get a new dataset is to explore it. In this section, we will learn the best practices for exploring and visualizing data.

Lesson Descriptions
Lesson 9: Introduction to Data Visualisation in Python (DataCamp)
Lesson 10: Preprocessing for Machine Learning in Python (New – DataCamp)
Lesson 11:  Feature Engineering for Machine Learning in Python (New – DataCamp)
Lesson 12: Feature Engineering for NLP in Python (New – DataCamp)

To understand your data and find out the hidden properties you need to account for, a grounding in basic statistical thinking is required. This is the core foundation to reliable machine learning models.

Lesson Descriptions
Lesson 13: Statistical Thinking in Python (Part 1) (DataCamp)
Lesson 14: Statistical Thinking in Python (Part 2) (DataCamp)
Lesson 15: Dimensionality Reduction in Python (New – DataCamp)

“Tell the machine that something is a cat over and over again, and it will start to learn the properties that separate cat pictures from other pictures”. Supervised learning is a machine learning task to learn a function that maps a given input to a given output (X -> Y). It is one of the most common and basic types of machine learning used in data science and AI.

Lesson Descriptions
Lesson 16: Supervised Learning with scikit-learn (DataCamp)
Lesson 17: Regression (Part 2) (New – AI Makerspace)
Lesson 18: Classification (Part 2) (New – AI Makerspace)
Lesson 19: Machine Learning with Tree-based Models in Python (DataCamp)
Lesson 20: Model Validation in Python (New – DataCamp)

Challenge Yourself

  • XGBoost Gradient Boosting (New)
  • Machine Learning for Time Series in Python (New)

Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. It is slightly more complex than supervised learning, as it requires human interpretation, but is still a key pillar of machine learning techniques used by data professionals.

Lesson Descriptions
Lesson 21: Unsupervised Learning in Python (DataCamp)
Lesson 22: Clustering Methods in SciPy (New – DataCamp)
Lesson 23: Unsupervised Learning (Part 2) (New – AI Makerspace)

Deep learning is a sub-type of machine learning where the algorithms used are inspired by biology. It is responsible for many of the breakthroughs in recent times, but is more complex and requires more computational power.

Lesson Descriptions
Lesson 24: Deep Learning in Python (DataCamp)
Lesson 25: Introduction to Tensorflow (New – DataCamp)
Lesson 26: Introduction to Keras (New – DataCamp)
Lesson 27: Deep Learning (Part 2) (New – AI Makerspace)

Challenge Yourself

  • Advanced Deep Learning with Keras (New)
  • Image Processing in Python (New)
  • Image Processing with Keras in Python (New)

Now that you have mastered the theoretical foundations of statistical learning and AI, how do you put it into practice in the real world?

Lesson Descriptions
Lesson 28: Data Science and AI Project Lifecycle (New – AI Makerspace)
Lesson 29: Set up your own AI System using an AI Makerspace ‘brick’ (New – AI Makerspace)

Machine learning and AI do not just stop at Python and Deep learning. There are many other software tools, concepts and languages that good data professionals will need to be conversant with as they progress in their career. A well balanced data professional will be equally skilled at software engineering and AI engineering.

Lesson Descriptions
Lesson 30: Introduction to SQL (DataCamp)
Lesson 31: Joining Data in SQL (DataCamp)
Lesson 32: Introduction to Shell (DataCamp)
Lesson 33: Conda Essentials 
Lesson 34: Hyperparameter Tuning in Python (New – DataCamp)
Lesson 35: Setting up your Environment (New – DataCamp)

Programme Structure

This programme is conducted online on AI Makerspace and includes a 12-month Datacamp subscription as a learning resource.

The modules are as follows:

  1. Introduction to Python
  2. Libraries and Data Manipulation
  3. Exploratory Data Analysis
  4. Statistical Thinking
  5. Supervised Learning
  6. Unsupervised Learning
  7. Deep Learning
  8. Other Programming Languages and Tools to Learn
  9. Data Science and AI in the Real World
Learners will receive the ‘Foundations in AI’ certificate upon successful completion of all modules 

Curriculum

The python programming language is one of the most popular languages that data professionals use to do data analytics and AI. In this section, you will practice Python basics which act as a foundation for the rest of the sections in this course.

Lesson Descriptions
Lesson 1: Introduction to Python (DataCamp)
Lesson 2: Python Data Science Toolbox (Part 1) (DataCamp)
Lesson 3: Python Data Science Toolbox (Part 2) (DataCamp)

What makes Python so powerful is that people like yourselves have written ‘libraries’ or toolkits that condense and optimize complicated programming methods to simple commands for us to use quickly and easily. Learning how to use libraries is a key skill in becoming a proficient data professional.

Lesson Descriptions
Lesson 4: Intermediate Python (DataCamp)
Lesson 5: Cleaning Data in Python (DataCamp)
Lesson 6: pandas Foundations (DataCamp)
Lesson 7: Manipulating DataFrames with pandas (DataCamp)
Lesson 8: Merging DataFrames with pandas (DataCamp)

The first order of business whenever you get a new dataset is to explore it. In this section, we will learn the best practices for exploring and visualizing data.

Lesson Descriptions
Lesson 9: Introduction to Data Visualisation in Python (DataCamp)
Lesson 10: Preprocessing for Machine Learning in Python (New – DataCamp)
Lesson 11:  Feature Engineering for Machine Learning in Python (New – DataCamp)
Lesson 12: Feature Engineering for NLP in Python (New – DataCamp)

To understand your data and find out the hidden properties you need to account for, a grounding in basic statistical thinking is required. This is the core foundation to reliable machine learning models.

Lesson Descriptions
Lesson 13: Statistical Thinking in Python (Part 1) (DataCamp)
Lesson 14: Statistical Thinking in Python (Part 2) (DataCamp)
Lesson 15: Dimensionality Reduction in Python (New – DataCamp)

“Tell the machine that something is a cat over and over again, and it will start to learn the properties that separate cat pictures from other pictures”. Supervised learning is a machine learning task to learn a function that maps a given input to a given output (X -> Y). It is one of the most common and basic types of machine learning used in data science and AI.

Lesson Descriptions
Lesson 16: Supervised Learning with scikit-learn (DataCamp)
Lesson 17: Regression (Part 2) (New – AI Makerspace)
Lesson 18: Classification (Part 2) (New – AI Makerspace)
Lesson 19: Machine Learning with Tree-based Models in Python (DataCamp)
Lesson 20: Model Validation in Python (New – DataCamp)

Challenge Yourself

  • XGBoost Gradient Boosting (New)
  • Machine Learning for Time Series in Python (New)

Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. It is slightly more complex than supervised learning, as it requires human interpretation, but is still a key pillar of machine learning techniques used by data professionals.

Lesson Descriptions
Lesson 21: Unsupervised Learning in Python (DataCamp)
Lesson 22: Clustering Methods in SciPy (New – DataCamp)
Lesson 23: Unsupervised Learning (Part 2) (New – AI Makerspace)

Deep learning is a sub-type of machine learning where the algorithms used are inspired by biology. It is responsible for many of the breakthroughs in recent times, but is more complex and requires more computational power.

Lesson Descriptions
Lesson 24: Deep Learning in Python (DataCamp)
Lesson 25: Introduction to Tensorflow (New – DataCamp)
Lesson 26: Introduction to Keras (New – DataCamp)
Lesson 27: Deep Learning (Part 2) (New – AI Makerspace)

Challenge Yourself

  • Advanced Deep Learning with Keras (New)
  • Image Processing in Python (New)
  • Image Processing with Keras in Python (New)

Now that you have mastered the theoretical foundations of statistical learning and AI, how do you put it into practice in the real world?

Lesson Descriptions
Lesson 28: Data Science and AI Project Lifecycle (New – AI Makerspace)
Lesson 29: Set up your own AI System using an AI Makerspace ‘brick’ (New – AI Makerspace)

Machine learning and AI do not just stop at Python and Deep learning. There are many other software tools, concepts and languages that good data professionals will need to be conversant with as they progress in their career. A well balanced data professional will be equally skilled at software engineering and AI engineering.

Lesson Descriptions
Lesson 30: Introduction to SQL (DataCamp)
Lesson 31: Joining Data in SQL (DataCamp)
Lesson 32: Introduction to Shell (DataCamp)
Lesson 33: Conda Essentials 
Lesson 34: Hyperparameter Tuning in Python (New – DataCamp)
Lesson 35: Setting up your Environment (New – DataCamp)

Note: the content covered may be subjected to change. Please refer to the administrative document provided after enrollment for the full list of lessons. 

Progressing beyond AI4I

Learners who completed AI4I modules are encouraged to progress by enrolling and completing “Becoming An AI Apprentice” course which is available FREE at AI Makerspace

Completion and mastery of the learning modules in “Becoming An AI Apprentice” course will allow the learner to attempt the technical test in the AI Certification Programme for the credentials of AI Certified Engineer Associate.  For more information, please refer to this page.

Pricing

AI4I™ COVID-19 initiative is sold out

Frequently Asked Questions (COVID-19 Initiatives)

Eligibility Criteria

Am I eligible for the AISG COVID-19 special course fee?

If you are a Singapore Citizen or Singapore Permanent Resident, you are eligible for the special course fee.

When I apply for the programme, how do I know if I have received the COVID-19 subsidies?

The special course fee will be reflected in the final price after entering your details in the application form

If I am an Employment Pass holder, am I eligible for the COVID-19 special course fee?

No. Only Singapore Citizens or Permanent Residents are eligible for the special course fee.

Application

How do I apply for the COVID-19 special course fee ?

You may register for the programme above. Please enter your full NRIC in the form to reflect the special course fee when you pay.

When is this COVID-19 special course fee available?

This special course fee is available from 1 May 2020 to 31 December 2020 for the first 2,500 eligible and successful registrations for AI4I™.

When is the deadline to register and qualify for the COVID-19 special course fee?

We are sponsoring 2500 participants for AI for Industry™. The special fee is extended to the first 2,500 eligible applicants only. The deadline to apply is 31 December 2020 or whichever comes first.

When is this COVID-19 special course fee available?

This special course fee is available from 1 May 2020 to 31 December 2020 for the first 2,500 eligible & successful registrations for AI4I™.

Programme Structure

With this AISG COVID-19 initiative, are there any changes to the programme requirements?

No. The programme structure and completion requirements are the same.

Company Sponsored Participants

Is the special course fee applicable to company sponsored participants?

Yes, the special course fee is applicable for employees who are Singapore Citizen or Permanent Resident. Please note that we will only accept payment via PayPal and we will not be issuing invoices to sponsoring companies benefiting from this subsidies.

Miscellaneous

I am a participant of the previous batches, can I be reimbursed the amount I paid previously?

No. This initiative takes effect from 1 May 2020. No refund or reimbursement will be provided for participants of the previous batches.

I am a participant of the previous batches. I have not completed any lessons. Can I request for a refund and apply for the programme again?

No. You may refer to our Terms and Conditions here.

Is the COVID-19 special price still available?

No, the COVID-19 special price has been fully redeemed.

What is the criteria for successful completion of the course?

You will have to achieve 100% score for all 9 quizzes from AI4I – Literacy in AI and 9 quizzes from AI4I – Foundations in AI

If I have an existing DataCamp subscription, do I have to cancel my subscription?

You can activate your new DataCamp subscription after your current subscription has expired.

Frequently Asked Questions

About the Course

Who is this programme for and what are the outcomes upon completion?

AI4I™ is a fully online programme meant to provide technical executives, managers and developers a learning experience in building data and AI application using Python. Upon completion, you will be able to build basic data/AI applications.

Will I get a certificate at the end of the course?

Yes, upon completion of the course within 12 months, you will be awarded the Foundations in AI Certificate issued by AI Singapore.

I am currently working. May I know what is the commitment expected for the course?

The course is flexible where you set your own learning pace. You are required to complete the course within 12 months in order to receive the ‘Foundations in AI’ certificate. 

What are the pre-requisites for the programme?

There are no pre-requisites to register for the programme.

Do I have to give up my current job to join the training programme?

No. This course is designed to enable professionals to continue to learn and up-skill at their own pace.

After completing this programme, will I be able to find a job as an AI Engineer/Developer?

This programme is the entry point to becoming an AI Engineer / Developer. The goal is to build foundational skills to start your AI learning journey. 

What if I decide to drop out of the course?

Please refer to our refund policy in the Terms & Conditions.

Course Requirements

Do I need to be a Singaporean/ Singaporean PR to apply?

No. We welcome all individuals to apply for the programme. 

What is the criteria for successful completion of the course?

You will have to complete all quizzes from the following topics on AI Makerspace

    • Introduction to Python
    • Libraries and Data Manipulation
    • Exploratory Data Analysis
    • Statistical Thinking
    • Supervised Learning
    • Unsupervised Learning
    • Deep Learning
    • Other Programming Languages and Tools to Learn
    • Data Science and AI in the Real World

DataCamp has 2 types of paid subscription for individuals. Which DataCamp plan will I be receiving?

Participants of AI for Industry™ will receive the ‘Premium’ plan which provides access to all courses and career tracks on DataCamp. For more information on the features available on DataCamp, you may visit here

Cost and Funding

What are the fees to join this training programme?

For a Singapore Citizen/PR, it is SGD224.70 (GST incl.). For the ‘Others’ category, it is SGD845.30 (GST incl.).

Can I use my SkillsFuture Credits?

No, we currently do not accept SkillsFuture Credits.

Are there subsidies available?

No, there are no subsidies available.

Can companies sponsor employees to join the programme?

Yes, please indicate your sponsorship status in the registration form. 

Kindly note that an invoice will be emailed to the contact person with payment instructions within 10 working days from the date of registration.

You are required to upload a letter of endorsement. You may find a template here

Miscellaneous

If I have an existing DataCamp subscription, do I have to cancel my subscription?

Please visit this article for more information.

I am a participant of AI4I™ Batch 4. I noticed that DataCamp has updated the 'Data Scientist with Python' career track. Should I update to the new track?

No. In order to qualify for CITREP+ claim, you are required to complete the previous version of the career track. For the full list of modules to be completed, you may visit here. 

If you have already updated to the new version of the career track, you can complete the list of modules (see above) individually, without the use of the career track. You will receive the ‘Foundations in AI’ certificate issued by AI Singapore. Please note that you will not receive the certificate issued by DataCamp.

What is the difference between the old 'Data Scientist with Python' career track and the current version that was updated in March 2020?

Click here to view the difference in the content between the 2 DataCamp career tracks. 

I have updated to the new 'Data Scientist with Python' career track on DataCamp. Can I switch back to the old track?

The change is not reversible after 24 hours from the time of change. 

Please indicate your interest here

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