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 early decades of the 20th century. Significant advances have been made since that time in the ability of machines to learning. And now today we see the emergence of apparently intelligent computer programs through the combination of massive computer power analysing massive amounts of data. One of the more popular jobs today is that of the data scientist, applying skills in statistics, artificial intelligence, machine learning, and data analysis, to gain insights from data.
AI, knowledge representation, and reasoning, Machine Learning algorithms, and Data Science skills have delivered new insights and understanding of our world. Whilst many of us see this technology as beyond us, it should not be. Yes, we are delivering sophisticated computer software that seems to behave intelligently, but we need to work to understand the technology, not to driven by some mysterious wizardry. AI has made its way into all of our hands, and it is incumbent upon us to understand it and for it to be able to explain itself.
The Machine Learning Hub (MLHub) is a framework and a repository, providing easy access and insights into AI, Machine learning, and Data Science. It supports us in freely and openly sharing our technology and experiences, to allow all of us to explore new ideas using this technology. As a repository of packages that capture pre-built demonstrations and models, the hub aims to ensure each package is demonstrable within 5 minutes.
The aim of this book is to get you quickly started with the MLHub, and to share our excitement through a simple and productive environment for exploring the state-of-the-art. The MLHub hides the underlying complexity to make the technology accessible. The MLHub repository houses a growing number of curated packages whilst allowing anyone to package their own models and have them available. 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.
After the introductions in the first few chapters of this book, 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 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 Graham.Williams@togaware.com Creative Commons Attribution-ShareAlike 4.0
