Artificial Intelligence and Machine Learning

Faculty of Information Technology, Science and Engineering – National Higher Certificate – Artificial Intelligence and Machine Learning – NQF Level 5 – 120 Credits

Higher Education Certificate – Artificial Intelligence and Machine Learning NQF Level 5

Course annual Cost : R25 000

Monthly Payments : R2 000 (12 Months)

Once off registration Fee : R2 000

Semester one theoretical modules : February – July

MODULE 1:Introduction to Machine Learning and Algorithms

History of Machine Learning and Artificial Intelligence

Building a Machine Learning System

Unsupervised Learning

Reinforcement Learning

Evaluating a Machine Learning System

Module 2 – Introduction to Machine Learning Development Platforms (MALDP 111)

Introduction to Python

AI and Machine Learning Tools, SDKs and Engines

Using Python and other AI Languages

MODULE 3: Probability Theory and Statistics with Python

Data Plotting in Python


Probability and Random Variables

Useful Probability Distributions


MODULE 4: Feature Engineering

Data Loading and Manipulation

Working on Images

Features and Feature Vectors

One-hot Encoding

Feature Normalization

MODULE 5: Supervised and Unsupervised Learning

Clustering using K-means Algorithm

K-Means Implementation

Clustering using Expectation-Maximization

Association Rules and Recommender Systems

MODULE 6: Scala and Java Programming

Introduction to Java

Programming on JVM and other IDEs using java

Introduction to Scala

Programing on JVM and other IDEs using Scala

MODULE 7: Feedforward Neural Networks

Mathematical Neural Models

The Perceptron

The Gradient Descent Algorithm

Multi-Layer Perceptron

MODULE 8: Convolutional and Recurrent Neural Networks

Deep Neural Networks

Convolutional Neural Networks

Recurrent Neural Networks

MODULE 9: Natural Language Processing – Part I

Problems Solved by Natural Language Processing

Text Preprocessing

Regular Expressions

Discrete Features

MODULE 10: Natural Language Processing – Part II

Word Embeddings

Part of Speech Tagging

Text Classification using Naïve Bayes

Text Classification using Neural Networks

MODULE 11: Practical Applications

Industrial Knowledge Representation using Decision Trees

Industrial Fault Diagnosis using Feedforward Neural Networks

Sound Classification using Feedforward Neural Networks

Image Classification using Convolutional Neural Networks

Machine Translation and Chatbots using Recurrent Neural Networks

MODULE 12: Web Deployment

Use of Flask

Integrating machine learning models with Flask

Deploying applications to a Web Server

Module 12– Communications and Self Development (ComSelf 111)

Learning and Reading

How to Study Effectively

Virtual and Face to Face Presentations

Time Management Tactics

Financial Management Tactics

Understanding Self Development

Ethics and Psychology

Entrepreneurship and Innovation

Entry Requirements

National Senior Certificate (NSC) with diploma or Higher Certificate entry or an equivalent foreign secondary qualification or international school-leaving certificate on an NSC level (NQF 4) confirmed by SAQA.


National Certificate (Vocational) level four issued by the Council of General, Further Education, and Training.

Course Duration

This course takes a minimum of one year and maximum of 3 years.

Theoretical work is covered over a period of Six months from enrollment from January until June. Practical work takes six months and begins in August until January the next year.

Students enrolling in July start theoretical work from July until December. Practical work commences in January and ends in June.

Career Options

Machine learning engineer

A highly coveted career, machine learning engineers are computer programmers with strong software skills who can apply complex predictive models, process large sets of data, and use natural language processing to programme machines to perform specific tasks that support a business’s goals. 

For a career as a machine learning engineer, a background in applied research and data science is beneficial, along with an in-depth understanding of programming languages such as Java, Python, and Scala. Previous exposure to an agile development environment will be useful, along with a master’s or doctoral degree in mathematics or computer science, and working knowledge of development tools like Eclipse and IntelliJ.

Data scientist

A data scientist’s primary role is to analyse, visualise, and model large volumes of data…

Data science is the heart of AI, automation, and machine learning. Data scientist roles have grown by 8% in 2018 but are projected to grow by 33% in the IT sector by 2022, with consistent demand for this role across almost all sectors.

A data scientist’s primary role is to analyse, visualise, and model large volumes of data to build and implement new machine learning models to support sound business decisions.

You need to be highly experienced in statistical computing languages and programming languages such as Perl, Python, SQL, and Scala. You should be familiar with big data platforms and tools, such as Hive, Hadoop, MapReduce, Pig, and Spark.

Business intelligence developer

Business Intelligence (BI) developers research and plan solutions for problems within a business and increase profitability by analysing complex data. They design, model, test and maintain cloud-based data storage systems and then analyse the data for trends in the market and business, thus improving the business’s overall profitability. A degree in computer science and engineering, with experience in data warehouse design, data mining, SQL queries and SQL Server Integration Services are necessary.

Software developer

The rapid increase in the number of computer systems and mobile apps that use software has resulted in software developers being in high demand with an estimated 302,500 new jobs forecast before 2026. A software developer oversees the entire development process of computer programs for a business and provides the best-suited software for the business. Software developers who focus on AI and machine learning should be adept at writing code, and in building and optimising large, complex systems.

Robotics scientist

A robotics scientist main role is to build and maintain robots…

Robotics and AI are quite different from each other, with artificially intelligent robots – robots that are controlled by artificial intelligence programmes – bridging robotics and AI. However, not all robots are artificially intelligent and non-intelligent robots are restricted in their functionality to perform a repetitive series of movements. AI algorithms are often necessary to allow the robot to perform more complex tasks.

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A robotics scientist’s main role is to build and maintain the robots that carry out the tasks in an organisation that still require human input by varying degrees. These are typically in sectors such as manufacturing, security, space and aerospace, and healthcare.24

Robotic scientists should have experience in robotic or mechanical engineering, with a background in advanced mathematics, physical science, and computer-aided design. The ability to create and edit computer programs, as well as work with specialists and subject-matter experts to develop prototypes is also an advantage.25

AI research scientist

An artificial intelligence research scientist should be operating at an expert-level in several AI disciplines, including machine learning, deep learning, computer perception, applied maths, and computational statistics.26 AI research scientists are predicted to have a great future, as they will be at the developmental cusp of artificial learning and machine learning applications for many years to come.27

Toby Walsh, an artificial intelligence professor at the University of New South Wales, says “I always joke that the safest job on the planet is AI researcher. When we’ve automated AI researchers, the machines will literally be able to do everything else by definition.”28

Most employees want their research scientists to possess an advanced master’s or doctoral degree in computer science or a related technical field with relevant experience. An in-depth understanding of benchmarking, parallel computing, distributed computing, machine learning, and artificial intelligence is also important.

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