R Basics, Data Science, Statistical Machine Learning models, Deep Learning, Shiny and much more (All R code included)
InstructorBert Gollnick
CouponThe curriculum covers a wide range of topics including regression, classification, clustering (k-means, DBSCAN), and deep learning architectures.
Instruction includes practical approaches to data manipulation, visualization with ggplot and leaflet, and model building through various lab sessions.
Editorial course preview
These 3 complementary views highlight concrete, legible examples from the course presentation.
This high-level course overview chart maps machine learning types like unsupervised and supervised against target variables to show where clustering, regression, and classification fit.
This interactive dashboard visualizes Receiver Operating Characteristics, allowing users to adjust thresholds and observe the impact on the confusion matrix and ROC curve.
This dashboard illustrates why multiple trees outperform a single decision tree by contrasting the blocky boundary of a single model with the smoother, more accurate boundary of a random forest.
One sampled review suggests that some learners have noted that code implementation can occur without sufficient explanation of the underlying logic or command purpose.
One signal indicates a transition to a BETA version of the course, which may impact the established learning experience.
The curriculum aligns with the target audience of R beginners and data science practitioners, though some signals suggest it may be difficult for those needing more conceptual depth.