Preface

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“The enjoyment of one’s tools is an essential ingredient of successful work.” Donald E. Knuth

Artificial Intelligence (commonly we just say AI) emerged in the middle decades of the 20th century, leading to significant capabilities being developed in Machine Learning and the extensive realisation of the role of Data Science in every organisation. We are in the midst of what we currently think of as the era of massive data and massive compute.

AI knowledge structures and reasoning, Machine Learning algorithms, and Data Science practises have certainly delivered new insights and understanding of our world. Whilst we might think this technology is beyond us, the fact that it is delivering sophisticated computer software that seems to behave intelligently should encourage us to be on top of the technology, not driven by the technology. AI is slowly making its way into the hands of us all, not only as users of systems built using the technology, but also for us all to be able to use the technology in new ways.

The Machine Learning Hub (MLHub) is a framework and a repository, providing easy access to AI, Machine learning, and Data Science. It supports freely and openly sharing our technology and experiences, and to allow others to explore new ideas using this technology and building on this technology. As a repository of packages that capture pre-built demonstrations and models, the hub aims to ensure each package is accessible and usable within 5 minutes.

The aim of this book is to quickly get started with the MLHub, and to share in the excitement through a simple and productive environment for exploring the state-of-the-art. The MLHub hides the complexity to make the technology accessible. The MLHub repository houses a growing number of curated packages. Each package demonstrates a different technology, quickly. If it looks useful then you can explore and utilise the technology through the package. If not, then move on, having spent only a few minutes to be impressed.

The MLHub is implemented using the popular and easy to learn Python programming language on the 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. 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 Chapter ?? for a guide to deploying Ubuntu on your computer and my GNU/Linux Desktop Survival Guide to delve much more into using GNU/Linux yourself.

After the introductions in the first few chapters of this book, the main body of the book is then a practical hands-on look at the different AI, Machine Learning, and Data Science packages available from the MLHub. The breadth of available packages is comprehensive, and the depth ranges from simple introductory technology to the current state-of-the-art algorithms. The focus is on making it easy for you to use the technology. For a more detailed exploration of AI, Machine Learning, and Data Science see the Data Science Desktop Survival Guide.



Your donation will support ongoing development 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.
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