This online workshop will provide a comprehensive introduction to the Classification models in Machine Learning and their application on real-world datasets using Python.
This course will explore the fundamentals of Machine Learning from a practical perspective with the help of the Python programming language and its scientific computing libraries. It will provide a comprehensive introduction to the Classification 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.
- Comprehensive introduction to Machine Learning models and techniques such as Logistic Regression, Decision Trees and Ensemble Learning.
- 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 2 of a 3 part series. It is strongly recommended you attend all 3 courses in the series.
- Fri 11 June: Introduction to Machine Learning using Python: Introduction & Linear Regression
- Tues 15 June: Introduction to Machine Learning using Python: Classification
- Thurs 17 June: Introduction to Machine Learning using Python: SVM & Unsupervised Learning
Pre-requisites are required for this course. Click here for details.