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DESCRIPTION:Machine Learning (ML) is a new way to program computers to solv
e 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 perspec
tive with the help of the R programming language and its scientific comput
ing packages.\n\n#### You'll learn:\n\n- Understand the difference between
supervised and unsupervised Machine Learning.\n- Understand the fundament
als of Machine Learning.\n- Comprehensive introduction to Machine Learning
models and techniques such as Linear Regression and Model Training.\n- Un
derstand the Machine Learning modelling workflows.\n- Use R and and its re
levant packages to process real datasets\, train and apply Machine Learnin
g models\n\n#### Prerequisites:\n\n- Either [Learn to Program: R](https://
intersect.org.au/training/course/r101/) and [Data Manipulation in R](https
://intersect.org.au/training/course/r201/) or [Learn to Program: R](https:
//intersect.org.au/training/course/r101/) and [Data Manipulation and Visua
lisation in R](https://intersect.org.au/training/course/r203/)needed to at
tend this course. If you already have experience with programming\, please
check the topics covered in courses above to ensure that you are familiar
with the knowledge needed for this course\, such as good understanding of
R syntax and basic programming concepts and familiarity with dplyr\, tidy
r and ggplot2 packages.\n- Maths knowledge is not required. There are only
a few Math formula that you are going to see in this course\, however ref
erences to Mathematics required for learning about Machine Learning will b
e 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.\n\n#### Why do this course:\
n\n- Useful for anyone who wants to learn about Machine Learning but are o
verwhelmed with the tremendous amount of resources.\n- It does not go in d
epth into mathematical concepts and formula\, however formal intuitions an
d references are provided to guide the participants for further learning.\
n- We do have applications on real datasets!\n- Machine Learning models ar
e introduced in this course together with important feature engineering te
chniques that are guaranteed to be useful in your own projects.\n- Give yo
u enough background to kickstart your own Machine Learning journey\, or tr
ansition yourself into Deep Learning.\n\nFor a better and more complete un
derstanding of the most popular Machine Learning models and techniques ple
ase consider attending all three Introduction to Machine Learning using R
workshops:\n\n- Introduction to Machine Learning using R: Introduction &am
p\; Linear Regression\n- Introduction to Machine Learning using R: Classif
ication\n- Introduction to Machine Learning using R: SVM &\; Unsupervis
ed Learning\n \n \n \n**For more information\, please click [here](http
s://intersect.org.au/training/course/r205).**
SUMMARY:Introduction to Machine Learning using R: Introduction & Linear Reg
ression at La Trobe Online
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