Professional Diploma in
Machine Learning

During your first class, you will explore the course content and format, including what you can expect from the programme and how it is assessed. You will learn ML concepts.

• ML definition within AI
• History of ML
• Overview of ML applications
• ML tasks
• ML statistical learning and algorithms
• Future of ML

Understand how machine learning methods and algorithms allow computers to learn and extract patterns from raw data for predictions and helping in decision making. 

• ML categories
• ML learning algorithms
• Supervised learning
• Unsupervised learning
• Exploratory data analytics

Learn the tools and methods used for building machine learning models.

• Programming language for ML model development
• Setup and install Python and R environments
• Jupyter Notebook and Google colab
• Database management systems
• Virtualisation
• Git / Github

Understand the machine learning process and learn how to build, train, test & evaluate a model using Python programming and libraries.

• Data collection 
• Data cleansing
• Loading data
• Building a model 
• Training a model 
• Testing a model 
• Evaluating a model 

During your final class, the subject matter expert will summarise the key topics covered during the programme. The Assessment will be outlined in detail with plenty of opportunity to ask any clarifying questions.