🇪🇸 La Biblia de la IA | 🇬🇧 The Bible of AI

International scientific and technical publication on the states of Artificial Intelligence | ISSN 2695-6411 |

Quia ingenii artificio est de civitatibus


Mis ‘7 mandamientos‘ |🇬🇧 My 7 commandments
  • Statistics Using Excel Succinctly

    Learn the ins and outs of Microsoft Excel’s statistical capabilities. Author Charles Zaiontz will help you familiarize yourself with an often overlooked but very powerful set of tools. With Statistics Using Excel Succinctly, you will be able to maximize your Excel skills.

  • Fundamentals of Data Visualization

    Guide to making visualizations that accurately reflect the data, tell a story, and look professional. It has grown out of my experience of working with students and postdocs in my laboratory on thousands of data visualizations.

  • Defining Artificial Intelligence |🇪🇸 Definiendo Inteligencia Artificial

    The starting point to develop the operational definition is the definition of AI adopted by the High Level Expert Group on artificial intelligence. To derive this operational definition we have followed a mixed methodology. On one hand, we apply natural language processing methods to a large set of AI literature. On the other hand, we carry out a qualitative analysis on 55 key documents including artificial intelligence definitions from three complementary perspectives: policy, research and industry.

  • Statistics Fundamentals Succinctly

    Statistics is the foundation of intelligent data analysis. Statistics Fundamentals Succinctly by Katie Kormanik provides the foundational bricks and mortar needed to master the theories and methodologies behind statistical procedures. In less than 100 pages, you’ll understand how to better gather and interpret all the information at your fingertips.

  • Advanced R

    Advanced R helps you understand how R works at a fundamental level. It is designed for R programmers who want to deepen their understanding of the language, and programmers experienced in other languages who want to understand what makes R different and special. This book will teach you the foundations of R; three fundamental programming paradigms (functional, object-oriented, and metaprogramming); and powerful techniques for debugging and optimising your code.

  • Artificial Intelligence: Foundations of Computational Agents

    It presents artificial intelligence as the study of the design of intelligent computational agents. The book is structured as a textbook, but it is accessible to a wide audience of professionals and researchers. In the last decades we have witnessed the emergence of artificial intelligence as a serious science and engineering discipline. This book provides an accessible synthesis of the field aimed at undergraduate and graduate students. It provides a coherent vision of the foundations of the field as it is today. It aims to provide that synthesis as an integrated science, in terms of a multi-dimensional design space that has been partially explored. As with any science worth its salt, artificial intelligence has a coherent, formal theory and a rambunctious experimental wing. The book balances theory and experiment, showing how to link them intimately together. It develops the science of AI together with its engineering applications.

  • Made With ML

    ⚠ To see the ‘Technical sheet‘ and ‘Table of associated records‘ go the bottom, Pag. 2, Pag. 3. ( 👩‍🎓 🖥 To go the platform, on the image). Main website: madewithml.com Summary Made With ML is a platform for the ML community to discover, build and share projects. Our goal is to create a learning space where all of… Leer más

  • Text Mining with R (A Tidy Approach)

    If you work in analytics or data science, like we do, you are familiar with the fact that data is being generated all the time at ever faster rates. (You may even be a little weary of people pontificating about this fact.) Analysts are often trained to handle tabular or rectangular data that is mostly numeric, but much of the data proliferating today is unstructured and text-heavy. Many of us who work in analytical fields are not trained in even simple interpretation of natural language.

    We developed the tidytext (Silge and Robinson 2016) R package because we were familiar with many methods for data wrangling and visualization, but couldn’t easily apply these same methods to text.

  • Data Science at the Command Line

    Today, data scientists can choose from an overwhelming collection of exciting technologies and programming languages. Python, R, Hadoop, Julia, Pig, Hive, and Spark are but a few examples. You may already have experience in one or more of these. If so, then why should you still care about the command line for doing data science? What does the command line have to offer that these other technologies and programming languages do not?

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