Explore the fundamentals of Machine Learning and get a comprehensive introduction to linear regression and model training in 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 Linear Regression and Model Training, and 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.
- Understand the difference between supervised and unsupervised Machine Learning.
- Understand the fundamentals of Machine Learning.
- Comprehensive introduction to Machine Learning models and techniques such as Linear Regression and Model Training.
- 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 1 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.