Introduction to Machine Learning using R: SVM & Unsupervised Learning
Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the R programming language and its scientific computing packages.
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 R and its relevant packages to process real datasets, train and apply Machine Learning models.
\Either \Learn to Program: R\ and \Data Manipulation in R\ or \Learn to Program: R\ and \Data Manipulation and Visualisation in R\needed to attend this course. If you already have experience with programming, please check the topics covered in the courses above and \Introduction to ML using R: Introduction & Linear Regression\ to ensure that you are familiar with the knowledge needed for this course, such as good understanding of R syntax and basic programming concepts, familiarity with dplyr, tidyr and ggplot2 packages, and basic understanding of Machine Learning and Model Training.\Maths knowledge is not required. There are only a few Math formula that you are going to see in this course, however references to Mathematics required for learning about Machine Learning will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them.\
DOI: 10.5281/zenodo.6423747
Licence: All Rights Reserved
Contact: training@intersect.org.au
Keywords: R
Additional information
Status: Active
Authors: Intersect Australia
Introduction to Machine Learning using R: SVM & Unsupervised Learning
https://intersect.org.au/training/course/r207
https://dresa.org.au/materials/introduction-to-machine-learning-using-r-svm-unsupervised-learning
Machine Learning (ML) is a new way to program computers to solve real world problems. It has gained popularity over the last few years by achieving tremendous success in tasks that we believed only humans could solve, from recognising images to self-driving cars. In this course, we will explore the fundamentals of Machine Learning from a practical perspective with the help of the R programming language and its scientific computing packages.
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 R and its relevant packages to process real datasets, train and apply Machine Learning models.
\\Either \Learn to Program: R\ and \Data Manipulation in R\ or \Learn to Program: R\ and \Data Manipulation and Visualisation in R\needed to attend this course. If you already have experience with programming, please check the topics covered in the courses above and \Introduction to ML using R: Introduction & Linear Regression\ to ensure that you are familiar with the knowledge needed for this course, such as good understanding of R syntax and basic programming concepts, familiarity with dplyr, tidyr and ggplot2 packages, and basic understanding of Machine Learning and Model Training.\\Maths knowledge is not required. There are only a few Math formula that you are going to see in this course, however references to Mathematics required for learning about Machine Learning will be provided. Having an understanding of the Mathematics behind each Machine Learning algorithms is going to make you appreciate the behaviour of the model and know its pros/cons when using them.\\
training@intersect.org.au
Intersect Australia
R