Preface

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

Today we continue to hear much about Artificial Intelligence (commonly we just say AI), Machine Learning, and Data Science. It seems this is technology that is beyond many of us, but it is delivering quite sophisticated computer software that seems to behave intelligently. AI knowledge structures and reasoning, Machine Learning algorithms, and Data Science practises have certainly delivered new insights and understanding of our world and how we can capture that as computer programs. But it seems to remain the realm of those in the know and with the experience.

Yet the technology is not accessible only because we have not made enough effort to make it accessible. The Machine Learning Hub (MLHub) is then an innovative software framework that provides easy access to AI, Machine learning, and Data Science. It is an ecosystem to freely and openly share our technology and experiences.

Through this book you will quickly get started with the MLHub, and will share in the excitement through s 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. Whilst not necessary for using the MLHub, you too can learn Python through many of the introductory resources available on the Internet. The GNU/Linux operating system is the most widely deployed operating system today, and is most productive for learning about, utilising, developing and deploying AI, Machine Learning, and Data Science. It has been the choice free and open source operating system 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, the main body of the book is 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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