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Machine Learning with R

5 Topics • 37 Exercises • 9 Quizzes

Get an introduction to all major (un-)supervised machine learning models and learn how to apply them in R. Employ such models in a business context with packages like caret, mlr and e1071.

Intermediate difficulty

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In this course...

  • 5 Topics
  • 37 Exercises
  • 9 Quizzes
  • Wide variety of topics
  • Unlock free course materials
  • Free quizzes and exercises
    • Key facts

    • Get an overview of the machine learning model zoo and choose the right model for each task.
    • Train your models without overfitting and optimize your resampling strategies based on the dataset.
    • Deploy your models into production processes based on your trained R-models.

    Chapters

    5 Topics • 37 Exercises • 9 Quizzes

    Recap Tidyverse

    A Recap of the most important tidyverse functions needed for this course.

    Introduction

    Learn the basic building blocks and terminology in machine learning. Differentiate between supervised and unsupervised learning.

    Model Fitting Basics

    See the basic model fitting processes with a simple regression and classification task.

    Resampling and Preprocessing

    Learn why resampling techniques are needed to fit machine learning models and apply the most important pre-processing steps to make most out of your data.

    The Model Zoo

    Get an overview of the model zoo including linear models, trees and support vector machines.

    Machine Learning Basics

    The explosion of data available in various areas like medicine, biology, marketing and finance requires more sophisticated modeling and computing tools such as machine learning and programming languages like R. During the basic module you learn why and when machine learning models should be used and how they compare to classical regression and classification models. Additionally you differentiate between supervised and unsupervised models and understand the bias-variance tradeoff including overfitting and underfitting.

    The Model Zoo

    Starting from simple models like linear regression and k-nearest-neighbors (KNN) you get a step-by-step introduction to the machine learning model zoo. The module covers linear as well as logistic methods for regression and classification, tree based models (including random forests and gradient boosting), neural networks and support vector machines. You will also learn basic concepts like data preparation, bagging and boosting, cross-validation. Models are compared and selected based on multiple case studies.

    Model Pipeline

    Put together your first model pipeline including explorative data analysis, data preparation, feature/model selection and deployment. Improve your model in each of these steps and determine when it is ready for production. Make models available to your users as REST APIs and manage the model development lifecycle including aspects such as model updates, monitoring, performance evaluation and scalability.

    Quantargo Certificate™

    The Quantargo certificate in “Machine Learning with R” demonstrates that your are literate in the topics covered by this course. You have...

  • In-depth knowledge on important concepts
  • Hands-on experience with the tools covered
  • A good starting point for further topics
  • Quantargo CourseCockpit™

    We have built a complete online Learning Hub with hands-on quizzes, code exercises and viedeos and an online Workspace that lets you immediately apply learned skills by trying out code.

    Track progress and learn in your own pace with our interactive tutorials. In the CourseCockpit you’ll find all assets and course materials.

    A complete RStudio environment to try out new ideas and play around with different packages and features. All files are synced automatically.