Introduction to Machine Learning using Python: SVM & Unsupervised Learning

Thu, 17/06/2021 - 09:30 to 12:30

This online workshop will provide a comprehensive introduction to Support Vector Machine and Unsupervised models in Machine Learning. It will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries. This course will provide a comprehensive introduction to Support Vector Machine and Unsupervised models in Machine Learning and use Python to apply the knowledge on real-world datasets. It will provide you with a better understanding of the Machine Learning models and techniques as well as an appreciation of its capability to enable you to make better informed decisions on how to leverage Machine Learning in your research.

Learning outcomes:

  • Comprehensive introduction to Machine Learning models and techniques such as Logistic Comprehensive introduction to Machine Learning models and techniques such as Support Vector Machine, K-Nearest Neighbor and Dimensionality Reduction.
  • Know the differences between various core Machine Learning models.
  • Understand the Machine Learning modelling workflows.
  • Use Python and scikit-learn to process real datasets, train and apply Machine Learning models

Please note: This course is part 3 of a 3 part series. It is strongly recommended you attend all 3 courses in the series.

  1. Fri 11 June: Introduction to Machine Learning using Python: Introduction & Linear Regression
  2. Tues 15 June: Introduction to Machine Learning using Python: Classification
  3. Thurs 17 June: Introduction to Machine Learning using Python: SVM & Unsupervised Learning

Pre-requisites are required for this course. Click here for details. 



Registration closing date

Event details

30
Seat availability
Free

Location

Key contact

Mrs Doris Margarita Harrison
doris.harrison@unsw.edu.au

Upcoming events

28/06/2021 - 13:30 to 29/06/2021 - 12:30
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19/07/2021 - 13:30 to 16:30
20/07/2021 - 13:30 to 16:30