@rstats In case you haven't seen it yet: Due to the Corona crisis #Springer publishers provide several #rstats books for free:
https://link.springer.com/book/10.1007/978-3-319-72347-1
https://link.springer.com/book/10.1007/978-1-4614-8687-9
https://link.springer.com/book/10.1007/978-1-4939-2122-5
https://link.springer.com/book/10.1007/978-3-319-46162-5
etc.
@rstats Maybe you know this list of free #rstats #books already, but it is still a great collection:
"29 Excellent Free Books to Learn all about R", April 24, 2019, Erik Karlsson
In my opinion, #R is very suitable for #MachineLearning. With R, machine learning can be easily integrated into usual #rstats data analysis workflows. #RPackages provide access to virtually all relevant machine learning algorithms like #NeuralNetworks, Support Vector machines (#SVM), #RandomForests, Extreme Gradient Boosting (#XGBoost), #WEKA algorithms, etc.
Does anyone of the @rstats group have further recommendations?
See reply for sources: 4 books on machine learning.
Four books on #MachineLearning with #R #rstats:
Dinov, I.D. (2018): Data Science and Predictive Analytics: Biomedical and Health Applications using R. https://link.springer.com/book/10.1007/978-3-319-72347-1
Irizarry, R.A. (2019): Introduction to Data Science. https://rafalab.github.io/dsbook/
James, G. et al. (2014): An Introduction to Statistical Learning. With Applications in R. https://link.springer.com/book/10.1007/978-1-4614-7138-7
Lantz, B. (2013): Machine Learning with R. https://subscription.packtpub.com/book/big_data_and_business_intelligence/9781788295864
Gerald Leppert, PhD 
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