Machine learning can be a powerful tool in the toolkit of any data professional. Whether you're aiming to become a data scientist or simply hoping to get more out of an interesting data set, learning to do machine learning with R can help you unlock a whole new world of insights.
This course follows another recent release, Linear Modeling in R, in our R course path. It includes five new missions and concludes with a new guided project.
What's covered in this course?
Introduction to Machine Learning covers the fundamentals of the machine learning workflow, which is the approach you'll use to build and tweak models regardless of the specific algorithm you're using.
Because Dataquest is focused on hands-on learning, rather than just telling you about the workflow, we'll walk you through it step-by-step as you build and optimize a model using the k-nearest neighbors algorithm
To do that, you'll use your R programming skills and the caret package for machine learning in R.
The course starts by walking you through building and evaluating a univariate model using error metrics and simple validation. Then, you'll go deeper into more advanced topics like multivariate models, cross validation, and hyperparameter optimization.
As with all Dataquest courses, through it all you'll be learning with bite-sized text chunks before immediately applying each new lesson by writing your own code.
At the end of the course, you'll put it all together with a more open-ended guided project that'll challenge you to build an accurate model for predicting car prices from the ground up.
Why do I need to learn this?
Machine learning is a critical skill for anyone interested in working as a data scientist. If R is your language of choice, then you'll need to have a solid grasp of at least the fundamentals for most data scientist jobs.
That means being able to apply machine learning to real-world data problems, and it means understanding what's actually happening under the hood so that you know what kind of solution to apply when you encounter a new problem.
Like all Dataquest courses, Introduction to Machine Learning in R covers both the underlying theory and the practical application. You'll need both to pass technical interviews and successfully apply machine learning models to real-world problems.
Even if you're not interested in working as a data scientist, machine learning skills can unlock valuable insights and help you spot patterns you'd never be able to see on your own.
And to be frank, building ML models can also be really fun!
The first mission of this course is free, so why not give it a shot? You might be surprised by how much and how quickly you learn!