Course
Supervised Learning in R: Regression
IntermediateSkill Level
Updated 01/2025RMachine Learning4 hr19 videos65 Exercises5,300 XP46,258Statement of Accomplishment
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Prerequisites
Introduction to Regression in R1
What is Regression?
In this chapter we introduce the concept of regression from a machine learning point of view. We will present the fundamental regression method: linear regression. We will show how to fit a linear regression model and to make predictions from the model.
2
Training and Evaluating Regression Models
Now that we have learned how to fit basic linear regression models, we will learn how to evaluate how well our models perform. We will review evaluating a model graphically, and look at two basic metrics for regression models. We will also learn how to train a model that will perform well in the wild, not just on training data. Although we will demonstrate these techniques using linear regression, all these concepts apply to models fit with any regression algorithm.
3
Issues to Consider
Before moving on to more sophisticated regression techniques, we will look at some other modeling issues: modeling with categorical inputs, interactions between variables, and when you might consider transforming inputs and outputs before modeling. While more sophisticated regression techniques manage some of these issues automatically, it's important to be aware of them, in order to understand which methods best handle various issues -- and which issues you must still manage yourself.
4
Dealing with Non-Linear Responses
Now that we have mastered linear models, we will begin to look at techniques for modeling situations that don't meet the assumptions of linearity. This includes predicting probabilities and frequencies (values bounded between 0 and 1); predicting counts (nonnegative integer values, and associated rates); and responses that have a non-linear but additive relationship to the inputs. These algorithms are variations on the standard linear model.
5
Tree-Based Methods
In this chapter we will look at modeling algorithms that do not assume linearity or additivity, and that can learn limited types of interactions among input variables. These algorithms are tree-based methods that work by combining ensembles of decision trees that are learned from the training data.
Supervised Learning in R: Regression
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FAQs
What prior R knowledge do I need before starting this regression course?
You should be comfortable with dplyr for data manipulation, ggplot2 for visualization, and basic statistics concepts like linear regression in R before enrolling.
Does this course cover both linear and nonlinear regression models?
Yes. You will work with linear regression, generalized additive models (GAMs), and tree-based models like random forests, learning when each approach is most appropriate.
How are models evaluated in this course?
You will learn to evaluate regression models using root mean squared error (RMSE) and R-squared, along with graphical residual analysis and cross-validation techniques.
What kinds of data science roles use these regression techniques?
Data scientists, machine learning engineers, and analysts regularly use supervised regression to forecast sales, predict prices, and estimate continuous outcomes in business.
How long does it typically take to complete all five chapters?
The course has 65 exercises across five chapters. Most learners finish in about four to five hours, though the pace depends on your experience level.
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