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Supervised Learning in R: Regression

4.1+
13 reviews
Intermediate

In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.

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4 Hours19 Videos65 Exercises
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Course Description

From a machine learning perspective, regression is the task of predicting numerical outcomes from various inputs. In this course, you'll learn about different regression models, how to train these models in R, how to evaluate the models you train and use them to make predictions.
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In the following Tracks

Certification Available

Associate Data Scientist in R

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Machine Learning Fundamentals in R

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Machine Learning Scientist with R

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  1. 1

    What is Regression?

    Free

    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.

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    Welcome and Introduction
    50 xp
    Identify the regression tasks
    50 xp
    Linear regression - the fundamental method
    50 xp
    Code a simple one-variable regression
    100 xp
    Examining a model
    100 xp
    Predicting once you fit a model
    50 xp
    Predicting from the unemployment model
    100 xp
    Multivariate linear regression (Part 1)
    100 xp
    Multivariate linear regression (Part 2)
    100 xp
    Wrapping up linear regression
    50 xp
  2. 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.

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  3. 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.

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  4. 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.

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GroupTraining 2 or more people?

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In the following Tracks

Certification Available

Associate Data Scientist in R

Go To Track

Machine Learning Fundamentals in R

Go To Track

Machine Learning Scientist with R

Go To Track

Datasets

BikesBlood PressureCricketHouse PricesIncomeMpgSoybeanUnemploymentSparrow

Collaborators

Collaborator's avatar
Sumedh Panchadhar
Collaborator's avatar
Richie Cotton
Nina Zumel HeadshotNina Zumel

Co-founder, Principal Consultant at Win-Vector, LLC

Nina is a co-founder and principal consultant at Win-Vector LLC, a San Francisco data science consultancy. She is co-author of the popular text Practical Data Science with R and occasionally blogs at the Win-Vector Blog on data science and R. Her technical interests include data science, statistics, statistical learning, and data visualization.
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John Mount HeadshotJohn Mount

Co-founder, Principal Consultant at Win-Vector, LLC

John is a co-founder and principal consultant at Win-Vector LLC, a San Francisco data science consultancy. He is the author of several R packages, including the data treatment package vtreat. John is co-author of Practical Data Science with R and blogs at the Win-Vector Blog about data science and R programming. His interests include data science, statistics, R programming, and theoretical computer science.
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Don’t just take our word for it

*4.1
from 13 reviews
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  • Thomas M.
    3 months

    Extremely useful and prefectly orchstrated!

  • Margit S.
    10 months

    The videos were very compact, but the tasks were all straightforward to solve in combination with the videos. The overview I was able to get with this course was excellent.

  • Nicolas F.
    about 1 year

    This course gives excellent resources through WVPlots and vtreat on how to develop and evaluate regression model performance.

  • Edwin A.
    over 1 year

    This is a great course to learn regression in supervised learning in R.

  • Mark R.
    over 1 year

    Very hands on, easy to follow instruction videos and useful practice exercises

"Extremely useful and prefectly orchstrated!"

Thomas M.

"The videos were very compact, but the tasks were all straightforward to solve in combination with the videos. The overview I was able to get with this course was excellent."

Margit S.

"This course gives excellent resources through WVPlots and vtreat on how to develop and evaluate regression model performance."

Nicolas F.

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