Who should attend
This course is intended for anyone interested in machine learning, coding and applying Python to solve machine learning problems.
About the course
Explore machine learning fundamentals and get started with popular algorithms. Get hands-on with a step-by-step tutorial on Python programming and how it's used to solve machine learning problems.
Machine learning enables rapid, automatic analysis of complex data in large volumes to deliver accurate results quickly. It can help organisations identify opportunities and avoid unknown risks.
This course provides participants with a fundamental background knowledge of the theory, approaches and history of machine learning and helps develop programming skills in Python for practical application to machine learning tasks and problems.
This course will cover the following content:
- Overview of modern-day data analytics and machine learning
- Machine learning fundamentals
- Introduction to Python and basic operations
- Cluster analysis
- Performing clustering algorithms
- Classification and regression
- Programming regression models and logistic regression
- Time series modelling
- Inventory modelling with autoregressive integrated moving average and Hidden Markov Model
- Recommendation systems
- Collaborative filtering and non-negative matrix factorisation
- Conclusion, action planning and next steps.
By the end of the course you will be able to:
- Explain to a peer how machine learning algorithms can be applied and the background behind these algorithms
- Program in Python to solve common machine learning problems
- Apply machine learning related Python tools, including numpy and scikit-learn libraries.
As an Associate Professor and Director of Industry Analytics and Visualisation, and the creator of at Faculty of Engineering & IT's DataLounge initiative, I am constantly navigating between the demands of developing world-renowned machine learning research and finding practical applications i...
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Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
We are happy to help you find a suitable online alternative.