This book is produced using bookdown. Emacs is used to edit the text. Many will be using RStudio to edit their bookdown documents, which is a generally more friendly environment and is the environment of choice for bookdown support. I’ve used Emacs since 1985 and as a fully extensible β€œkitchen-sink” type of editor, it has served me well for over 35 years, despite numerous flirtations with β€œbetter” editors over my career. RStudio and Visual Studio Code come close to supporting all that is required, but the flexibility provided by Emacs still makes it the leading and most mature integrated development environment (IDE).

Bookdown is an rmarkdown based platform for intermixing text with executable code (like Python, R and Shell code blocks). Rmarkdown itself utilises the simple markdown syntax to markup the sections of a document. After running knitr over the rmarkdown material a markdown document is produced, including the output of any commands that were run.

Pandoc is utilised to produce html from the markdown document. this can be publsihed to the world wide web. It can also produce pdf output utilising LaTeX, converting the markdown into LaTeX markup, with xetex used to then convert that to pdf.

All these tools are open source software and available on multiple platforms, and all for free.

Many books are today being written using bookdown. Examples include Data Science at the Command Line (github); Efficient R Programming (github).

The MLHub repository itself is implemented using the popular and easy to learn Python programming language on the free and open source Ubuntu distribution of the GNU/Linux operating system. Pacakges are implemented in Python or R. Whilst not necessary for using the MLHub, you too can learn Python or R through many of the introductory resources available on the Internet, including the Data Science Desktop Survival Guide.

The GNU/Linux operating system is the target platform for the MLHub whilst also usable on Apple/MacOS and MS/Windows. GNU/Linux is the most widely deployed operating system today, available, for example, from the Microsoft Store under the Windows Subsystem for Linux. It is also a most productive environment for learning about, utilising, developing and deploying AI, Machine Learning, and Data Science. It is a free and open source operating system continually being improved by thousands of developers for over 30 years. See the GNU/Linux Desktop Survival Guide for a guide to deploying Ubuntu on your computer and to delve much more into using GNU/Linux yourself.

Your donation will support ongoing availability and give you access to the PDF version of this book. Desktop Survival Guides include Data Science, GNU/Linux, and MLHub. Books available on Amazon include Data Mining with Rattle and Essentials of Data Science. Popular open source software includes rattle, wajig, and mlhub. Hosted by Togaware, a pioneer of free and open source software since 1984. Copyright © 1995-2022 Creative Commons Attribution-ShareAlike 4.0