Gerald Leppert, PhD Apr 10, 2020

@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.

#SpringerLink #R

Data Science and Predictive Analytics | SpringerLink

Over the past decade, Big Data have become ubiquitous in all economic sectors, scientific disciplines, and human activities. They have led to striking technological advances, affecting all human exper

Show threadGerald Leppert, PhD Apr 18, 2020

@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

https://www.linuxlinks.com/excellent-free-books-learn-r/

#R

33 Excellent Free Books to Learn all about R - LinuxLinks

This article recommends 33 free books which will teach you the basics of R, how to produce amazing plots, how to apply R to lots of disciplines, and how to efficiently program in R.

LinuxLinks
Show threadAskan 🇪🇺
@gerald_leppert @rstats is R a good tool for machine learning?
Apr 19, 2020 at 8:03amWeb
Show threadGerald Leppert, PhD Apr 19, 2020

@askans

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.

Show threadGerald Leppert, PhD Apr 19, 2020

@askans @rstats

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

Data Science and Predictive Analytics | SpringerLink

Over the past decade, Big Data have become ubiquitous in all economic sectors, scientific disciplines, and human activities. They have led to striking technological advances, affecting all human exper

Show threadAskan 🇪🇺Apr 19, 2020
@gerald_leppert @rstats Danke schön
Show threadAskan 🇪🇺Apr 19, 2020
@gerald_leppert @rstats Danke Dir
Show threadEdenApr 20, 2020

@askans @gerald_leppert @rstats Tabular ML, absolutely.

LightGBM is simpler in R than it is in Python, and it works pretty well. Caret is miles better than other options in Python AFAICT.
BSTS _only_ exists in R & C++.

Working with anything that doesn't fit easily into data.frames/tables, it's just not well suited because of the issues with speed, and R is fastest with matrix & data.frame calculations.

NNs & Keras etc, it's better working in Python.

Show threadAskan 🇪🇺Apr 21, 2020
@Eden @gerald_leppert @rstats Thanks for your post, I am getting smarter.