So you want to become an AI Apprentice?

The AI Singapore’s AI Apprenticeship Programme (AIAP) is one of AI Singapore’s popular programme, with on average 120-160 applicants everytime we open for an intake. It is also rumoured to be very hard to get into, according to friendly sources from the ground.

This article will share what it takes to get into the AIAP, and map out a training roadmap that someone who is keen to join the AIAP should pursue. As mentioned in my earlier article *Excuse me, are you a Singaporean AI Engineer?”, the AIAP is a programme where you get to deepskill and work on a real-world AI project over 9 months. It is not a re-skilling programme where you come in to train and learn Python.

Do note that our expectations of what an Artificial Intelligence (AI) Engineer or Machine Learning (ML) Engineer will do go beyond just ML modeling. The AI Engineer is expected to ingest data, do feature engineering, build, train and test the model, and lastly deploy it at scale with ability of the model to be retrained and refined whenever required.


What we look for in an AI Apprentice?

One of the most valued traits in our AI Apprentices, is that all of them are self-starters. We are looking for individuals who are self-directed learners and keen to learn data science (DS), AI, and ML  from everyone and everywhere, and anyhow. 

They are curious, they search, hunt and dig to learn. They do not ask “tell me how?” or “where can I go to learn?”.

Oh, and if you are one of those that need to pay money to attend a classroom to learn about Python programming, then you are also likely not someone we will be keen on.


Recommended Learning Journey

Here is a 12-months intensive roadmap to help you in your journey to prepare for the AI Apprentice entrance test if you are keen to join the programme, otherwise, you can also use this for your own AI/ML learning journey. We assume you are starting with at least the following:

Python Programming

You have some programming background, if not, please pick up few good Python books or you can review the Python tutorial here or Microsoft’s new 44-part videos on Python here


You understand what a database is and can run some basic SQL queries, if not, you may wish to review SQL tutorials here:


12-months AI/ML Learning Roadmap Overview

SN PROGRAMME MODULES 1 2 3 4 5 6 7 8 9 10 11 12
1 Software Engineering X X                    
2 Statistical Learning, Machine Learning and Deep Learning                        
a1 Statistical Learning @ Stanford     X X X              
a2 The Data Science Design Manual Course     X X X              
b Intel AI Academy         X X X X X X    
c Azure Machine Learning Service                     X  
3 Spark Big Data Platform                       X
4 Kaggle competition (for some near real world experience)                 X X X X


Recommended Book List
I have not put a link or book image as some of these books gets updated often.

Learning Python:

  1. Python Crash Course, 2nd Edition: A Hands-On, Project-Based Introduction to Programming by Eric Matthes
  2. Learning Python by Mark Lutz
  3. Fluent Python: Clear, Concise, and Effective Programming by Luciano Ramalho

Machine Learning:

  1. The Hundred-Page Machine Learning Book by Andriy Burkov
  2. The Data Science Design Manual by Steven Skiena
  3. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney
  4. Python Data Science Handbook: Essential Tools for Working with Data by Jake VanderPlas
  5. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurelien Geron
  6. Deep Learning with Python by Francois Chollet

AI (Beyond Machine Learning):

  1. Artificial Intelligence: A Modern Approach by Stuart Russell

General AI:

  1. On Intelligence: How a New Understanding of the Brain Will Lead to the Creation of Truly Intelligent Machines by Jeff Hawkins
  2. Weapons of Math Destruction by Cathy O’Neil
  3. Rise of the Robots: Technology and the Threat of a Jobless Future by Martin Ford


AI and Ethics

A good friend reminded me we need to ensure AI and Ethics is part of this learning journey. Here are some recommended reading materials:

  1. Singapore’s IMDA AI Ethics and Governance framework (See ). Download PDF.
    • AI Singapore is leveraging this framework as part of our 100E projects engineering best practice.
    • Over the next 5 years, AI Singapore ‘s 100E programme would have delivered around 100 AI projects, and we will use this opportunity to help refine the framework providing useful real-world feedback to the team.
  2. Weapons of Math Destruction by Cathy O’Neil is a recommended read.
  3. Microsoft’s take on AI Ethics

Detailed Training Curriculum Click here

Source:AI Marketspace

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