YoVDO

Create machine learning models with R and tidymodels

Offered By: Microsoft via Microsoft Learn

Tags

Machine Learning Courses Data Analysis Courses Data Visualization Courses R Programming Courses ggplot2 Courses Regression Models Courses Classification Models Courses dplyr Courses tidyr Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
  • Module 1: In this module, you'll explore, analyze, and visualize data by using the R programming language.

    In this module, you'll learn:

    • Common data exploration and analysis tasks.
    • How to use R packages such as ggplot2, dplyr, and tidyr to turn raw data into understanding, insight, and knowledge.
  • Module 2: Introduction to regression models by using R and tidymodels.

    In this module, you'll learn:

    • When to use regression models.
    • How to train and evaluate regression models by using the tidymodels framework.
  • Module 3: Learn how to train classification models by using the R programming language and tidymodels framework.

    In this module, you'll learn:

    • When to use classification.
    • How to train and evaluate a classification model by using the tidymodels framework.
  • Module 4: Introduction to clustering models by using R and tidymodels.
    • When to use clustering models
    • How to train and evaluate clustering models by using the tidymodels framework

Syllabus

  • Module 1: Module 1: Explore and analyze data with R
    • Introduction
    • Exploratory data analysis
    • Exercise - Transform data by using dplyr
    • Visualize your data
    • Exercise - Visualize your data by using ggplot2
    • Examine real-world data
    • Exercise - Examine real-world data
    • Knowledge check
    • Challenge - Data exploration
    • Summary
  • Module 2: Module 2: Introduction to regression models by using R and tidymodels
    • Introduction
    • What is regression?
    • Exercise - Train and evaluate a regression model
    • Discover new regression models
    • Exercise - Experiment with more powerful regression models
    • Improve models with hyperparameters
    • Exercise - Optimize and save models
    • Knowledge check
    • Challenge - Regression
    • Summary
  • Module 3: Module 3: Introduction to classification models by using R and tidymodels
    • Introduction
    • What is classification?
    • Exercise - Train and evaluate a binary classification model
    • Evaluate classification models
    • Exercise - Train a classification model by using alternative metrics
    • Create multiclass classification models
    • Exercise - Train and evaluate multiclass classification models
    • Knowledge check
    • Challenge - Train a classification model to classify wine data
    • Summary
  • Module 4: Module 4: Introduction to clustering models by using R and tidymodels
    • Introduction
    • What is clustering?
    • Exercise - Train and evaluate a clustering model
    • Evaluate different types of clustering
    • Exercise - Train and evaluate advanced clustering models
    • Knowledge check
    • Challenge - Clustering
    • Summary

Tags

Related Courses

Introduction to Artificial Intelligence
Stanford University via Udacity Natural Language Processing
Columbia University via Coursera Probabilistic Graphical Models 1: Representation
Stanford University via Coursera Computer Vision: The Fundamentals
University of California, Berkeley via Coursera Learning from Data (Introductory Machine Learning course)
California Institute of Technology via Independent