Statistical Society of Australia warmly invites you to a workshop on machine learning with Python, presented by a data scientist from Eliiza (to be announced).

Workshop content

Day 1 Getting Started with Machine Learning

This is a hands-on course for making predictive models using machine learning.

We will use Python libraries such as pandas and scikit-learn to analyse a dataset and make a predictive model.  We will then discuss ideas such as the bias-variance tradeoff for improving machine learning models and apply it to the models built earlier. Throughout the workshop you will program a sequence of Jupyter notebooks and gain experience in working with data in Python. The workshop will conclude with a discussion of how to deploy machine learning models into real world systems.

At the end of this module you will be able to:

• Use the Python libraries pandas and numpy to import and manipulate data.
• Use scikit-learn to construct linear and tree-based models.
• Know the difference between classification and regression.
• Evaluate a predictive model with appropriate metrics and plots.
• Improve a machine learning model using hyperparameter tuning.

9 am–12:30 pm

Presenter: a data scientist from Eliiza (to be announced)

Day 2 Introduction to Deep Learning

This workshop will teach you how to use the TensorFlow 2.0 framework to construct neural networks and apply them to tasks such as image recognition.

Neural networks are a family of machine learning models that can take data in a wide variety of formats and learn non-linear patterns in data by training millions of parameters simultaneously. Neural networks, also known as “Deep Learning”, have become more popular since they were used to win the 2012 ImageNet Challenge. 

The workshop will cover:

• Use the Python libraries pandas and numpy to import and manipulate data.
• Use scikit-learn to construct linear and tree-based models.
• Know the difference between classification and regression.
• Evaluate a predictive model with appropriate metrics and plots.
• Improve a machine learning model using hyperparameter tuning.

9 am–12:30 pm

Presenter: a data scientist from Eliiza (to be announced)

Timetable

Day 1

Day 2

Expenses:

Occasionally workshops have to be cancelled due to a lack of subscription. Early registration ensures that this will not happen. Please note that the Society will not be held responsible for any financial loss incurred due to a workshop cancellation.

Financial Support:

Financial support for SSA Vic members can be sought. For further information, please see: https://sites.google.com/view/ssavicworkshopfinsup.

Contact:

Please contact the organisers: Rheanna Mainzer (rheanna.mainzer@gmail.com), Anna Quaglieri (anna.quaglieri@eliiza.com.au), and Patrick Robotham (patrick.robotham2@gmail.com) for further details.