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