- Level 5
- 10 Weeks
- Intakes in April, August
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
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
- 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
- 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.
Additional Micro Credentials
QRC Micro Credential in Responsible AI.
Level 5 | 10 Credits | 3 Weeks
Programme at a glance.
Graduates will be able to perform entry-level responsible AI tasks on AI and Machine Learning systems, both as part of a team and independently under broad supervision.
- The components of Responsible AI that are relevant to contemporary scenarios
- Distinguishing uses and end users of AI systems
- Using ethics to inspect aspects of datasets
- Comparing the different roles that are responsible and accountable for an AI system
- An ethical evaluation of an AI system
QRC Micro Credential in Machine Learning Team Project.
Level 5 | 10 Credits | 3 Weeks
Programme at a glance.
Graduates of this micro credential are able to demonstrate and apply effective communication, collaboration, and problem-solving skills to present a responsible machine learning system.
- Understanding the relationship of Machine Learning with Responsible AI and business ethics
- Collaborating, problem solving, and communicating in teams to improve machine learning systems
- Presenting findings and results of machine learning systems to internal stakeholders
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
This Level 5 programme consists of one ten-week block on campus with additional credentials available.
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
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:
- Suitable for School leavers, Career changers, Up–skillers 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.
- All applicants will require a pre-course discussion with the Academic Director to align course suitability.
- No recognition of prior learning, cross credit or credit transfer is available for this micro credential.
|2023 Domestic Tuition Fees|
|Machine Learning Fundamentals (40 Credits)||$2,400|
|Responsible AI Micro-Credential: (10 credits)||$600|
|Machine Learning Team Project Micro-Credential (10 Credits)||$600|
|2023 International Tuition Fees||International|
|Machine Learning Fundamentals (40 Credits)||$6,114|
|Responsible AI Micro-Credential: (10 credits)||$1,528|
|Machine Learning Team Project Micro-Credential (10 Credits)||$1,528|
|Intake One||21st November - 16th December 2022
17th December 2022 - 8th January 2023 Christmas Break
9th January - 17th February 2023
20th February - 24th February 2023 - Recess
27th February - 6th April 2023
|Intake Two||11th April - 16th June 2023
19th June - 23rd June 2023 Recess
26th June - 4th August
|Intake Three||21st August - 27th October 2023
30th October - 3rd November 2023 Recess
6th November - 15th December 2023
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.
Finding the dream job:
Machine Learning is technology that lets a computer get smarter and smarter all by itself just by looking at data.