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
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
MODULE 5: Supervised and Unsupervised Learning
Clustering using K-means Algorithm
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 Gradient Descent Algorithm
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
MODULE 10: Natural Language Processing – Part II
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
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.
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.
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.
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.
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.
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.
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.