• Level 5
  • 10 Weeks
  • queenstown
  • Intakes in 21st November 2022

There is an acute worldwide shortage of machine learning experts and intense international competition for talent. Machine learning is the highest demand growth tech skillset globally

This micro-credential in Machine Learning Fundamentals will take students with no prior experience* to a stage where they have the skills to be industry-ready upon graduation.

Machine Learning (ML) is the process of building, and using, predictive models. Artificial Intelligence (AI) is the software that surrounds these predictive models while in use, allowing software applications to become more accurate at predicting outcomes.

The qualification will focus on the foundations of machine learning including:

  • Types of machine learning algorithms including supervised and unsupervised learning
  • Regression and Classification techniques for machine learning applications
  • Using existing machine learning libraries to generate predictions
  • Constructing and documenting datasets
  • Understanding and applying Responsible AI and business ethics to machine learning systems
  • Applying and improving deep learning systems

This course is partially funded by TTAF

  • You can study this course – Fundamentals of Machine Learning Certificate (Level 5) partially for free until December 31st 2022.
  • The first four weeks of this course are funded by TTAF based on the start date of 21st November 2022
  • From 1st January 2023 course fees will be implemented
  • This will not impact your ability to use the Governments Fees Free scheme for further study (check your eligibility here)
  • Additional costs such as course materials and equipment are NOT included
  • This is not available for international students 

Programme at a glance

10 Weeks | Level 5 | Credits: 40

Qualification completed as part of this programme: Certificate in Machine Learning Fundamentals (Level 5) (Micro-credential)  

Delivered at our Queenstown Campus

Taught Content:

  • Module 1 – Introduction to Machine Learning
  • Module 2 – Predicting Unknown Values with Machine Learning Models
  • Module 3 – Constructing and documenting datasets
  • Module 4 – Responsible AI and Business Ethics
  • Module 5 – Introduction to Deep Learning

Learning Outcomes:

  • Discuss the difference between supervised and unsupervised learning.
  • Distinguish between classification and regression in machine learning
  • Use machine learning libraries to predict independent variables.
  • Present performance results of a machine learning model.
  • Construct and document a data set from raw data.
  • Describe and present the importance of implementing responsible AI systems that impact Māori and Pasifika communities.
  • Identify how to correct an imbalance in a dataset applying business ethics.
  • Select and justify a machine learning model to solve a classification task.
  • Apply deep learning models to generate predictions for tabular data.
  • Select and adjust hyperparameters for deep learning models to improve performance.

The number of Machine Learning engineers has increased tenfold in the past five years. Machine Learning qualifications providing introductory concepts, and practical skills necessary to support and develop ML in various industries will be an essential part of IT services in the future.

Graduates of the micro-credential will be able to apply the fundamentals of Machine Learning to entry level roles in the New Zealand tech, corporate and Health and Education sectors as: 

  • Entry-level Data Analysts 
  • Entry-level Business Intelligence Analysts 
  • Entry-level Machine Learning Engineer 

New Zealand must position itself for the future and machine learning/artificial intelligence are exponential technologies that are changing the world. We must take bold steps to further develop the capability in New Zealand or we will be left behind

Mike Marr CEO TPT Group Holdings Ltd

This Level 5 programme consists of one ten-week block on campus.

Utilising a blended delivery approach, 5 modules are taught over the course duration of 10 weeks.  Students will learn by doing and undertake project-based learning activities.  Provided with the preparatory resources that highlight the necessary concepts behind machine learning patterns and practices, students will be further supported by online resources such as Google Colab to provide programming environments for machine learning.

In addition to the core competencies required of machine learning practitioners, students will hone presentation skills, team collaboration and a suite of 21st century skills to build a well-rounded professional skill set.

The micro-credential delivery will be progressively assessed through online Moodle quizzes, presentations and a Portfolio of Evidence

Pathways into Industry

QRC aligns learning and training directly to that of industry needs and requirements which provides a valued outcome for learners.

Entering into Industry: Aligning supply & demand

This micro credential is in response to a united industry voice that is  facing demand for entry level machine learning operators for current and
future work streams across key businesses both domestically and globally.

The focus is on developing a tech eco system in which entry level machine learning operators can understand  and apply the fundamentals of machine learning to solve business tasks.

However, “a lack of people skills” has been identified as a problem within the industry.  For machine learning and AI employers, “soft skills are seen as very important to critically important”, especially in client-facing industries.  These soft skills facilitate responsible AI, as they promote empathy and understanding others lived experiences.

This qualification teaches the necessary machine learning and AI skills, along with the equally important soft skills.

Applicants must meet the following entry requirements:

✔ This micro credential is suitable for school leavers, career changers, upskillers already in the business sector who want to expand their knowledge and skills in machine learning to meet the need for entry level machine learning operators. 

Entry is open with the requirement of applicants being interviewed to ensure course suitability.

All applicants are interviewed as part of the admissions process. No Recognition of Prior Learning, cross credit or credit transfer is available for this programme.


TTAF is only available until 31st December 2022. From 1st January 2023 regular fees will apply.
Tuition Domestic
40 Credits: First 4 weeks Funded by TTAF*
40 Credits: Final 6 weeks $1,440
60 Credits: first 4 weeks Funded by TTAF*
60 Credits: Final 6 weeks $2,880
Tuition International
40 Credits: 10-weeks $6,114
60 Credits: 5-months $9,171

Enquire Today


Supervised vs Unsupervised Learning:

Broadly speaking, you can put most machine learning algorithms into one of two categories – supervised vs unsupervised. If you realise that you need to use machine learning to solve a problem, you will need to determine what kind of method you will use.

Read more>>

Machine Learning

Finding the dream job:

Machine Learning is technology that lets a computer get smarter and smarter all by itself just by looking at data.

Read more>>