In view of the 2019 novel coronavirus (2019-nCoV) situation and recent risk assessment to DORSCON Orange, please be informed that all AI for Industry face-to-face workshops for existing batches will be LIVE STREAMED until further notice.

Details on the LIVE streaming will be shared via Eventbrite for those who have registered for the respective workshop.

Registration for AI for Industry Batch 5 will open on 24 Feb 2020

AI for Industry

AI for Industry (AI4I) is an online programme to help technically inclined individuals understand and use AI appropriately and be able to program basic AI and data applications in Python. The programme is hosted on the AI Makerspace online platform.  DataCamp  is used as a resource to support the learning required in order  to complete the programme on AI Makerspace.

Programme Updates

Programme Structure

This programme will now be conducted online on AI Makerspace. Participants are required to complete the programme within 12 months.

The updated topics 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

Upon successful completion of all AI4I modules in AI Makerspace, trainees will receive the ‘Foundations in AI’ certificate.

This programme is part of AI Singapore’s AI Certification framework and trainees are encouraged to continue their learning after completing the AI4I programme. The next step upon completion is to be certified as an AI Associate Engineer
(professional certificate).  For more information on AISG’s AI Certification or about an AI Associate Engineer, please refer to this page.

Registration for Batch 5 will open on 24 February 2020

Registration Period: 24 Feb 2020 - 15 March 2020

Updated 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
Lesson 2: Python Data Science Toolbox (Part 1)
Lesson 3: Python Data Science Toolbox (Part 2)

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
Lesson 5: Cleaning Data in Python
Lesson 6: pandas Foundations
Lesson 7: Manipulating DataFrames with pandas
Lesson 8: Merging DataFrames with pandas

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
Lesson 10: Preprocessing for Machine Learning in Python (New)
Lesson 11:  Feature Engineering for Machine Learning in Python (New)
Lesson 12: Feature Engineering for NLP in Python (New)

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)
Lesson 14: Statistical Thinking in Python (Part 2)
Lesson 15: Dimensionality Reduction in Python (New)

“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
Lesson 17: Regression (Part 2) (New)
Lesson 18: Classification (Part 2) (New)
Lesson 19: Machine Learning with Tree-based Models in Python
Lesson 20: Model Validation in Python (New)

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
Lesson 22: Clustering Methods in SciPy (New)
Lesson 23: Unsupervised Learning (Part 2) (New)

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
Lesson 25: Introduction to Tensorflow (New)
Lesson 26: Introduction to Keras (New)
Lesson 27: Deep Learning (Part 2) (New)

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)
Lesson 29: Set up your own AI System using an AI Makerspace ‘brick’ (New)

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

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

Updated Pricing

How to Register

Step 2.

Register online

Step 3.

Pay using PayPal

Step 4.

Start learning on course start date

Frequently Asked Questions

About the Course

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

AI4I is an online programme meant to provide technical executives, managers, 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. However, if you wish to have an understanding of how the programme content is conducted, you may complete the ‘Literacy in AI‘ module on Makerspace. 

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 being an AI Engineer / Developer. The goal to is build foundational skills to start your AI learning journey. 

What is XP?

XP is a way of gauging how well you are doing or how engaged you are in DataCamp. It is calculated automatically based on courses, exercises or other actions you complete in DataCamp. Your total or cumulative XP will appear in the top right-hand corner of your screen and on your profile page. Whenever you choose the option to take a Hint or Show Solution, XP will be deducted from your potential additionally awarded XP.

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

Must I attend the face-to-face workshops? What if I am unable to attend them due to my work commitments?

In order to complete the programme, you will need to achieve 75% attendance for the face-to-face workshops. We will be conducting multiple sessions for each workshop to enable our participants to fulfill this requirement. Each workshop will have 1 session during work hours and 1 session after work hours. 

I am not a Singaporean / Singapore PR but I would like to attend the programme. What can I do?

Our programme is based on DataCamp’s content. Hence, you may sign up with DataCamp directly without signing up for our programme. The main difference is that our AI4I – Practical Foundations in AI with Python programme includes guidance from AI Singapore’a mentors as well as additional content found on AI Makerspace

Cost and Funding

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

What are the fees to join this training programme?

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. 

Miscellaneous

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

Please visit this article for more information.

Please indicate your interest here

Mailing List Sign Up

Registration Process (currently closed)

Step 1

Register an account in AI Makerspace.

Step 2

Complete AI4I Pre-work under the tab ‘LearnAI’

Step 3

After completion, you will receive an email invitation in your registered email address to apply for AI for Industry (AI4I).

Step 4

Complete the registration form and make payment using the registration link in the email invitation.

Step 5

You will receive the payment receipt and the welcome email within 7 business days.

Step 6

You will be able to access Datacamp premium on 10 Sep 2019, Eastern Time to start the AI4I programme.

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