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Practical Machine Learning with R
This textbook is a comprehensive guide to machine learning and artificial intelligence tailored for students in business and economics. It takes a hands-on approach to teach machine learning, emphasizing practical applications over complex mathematical concepts. Students are not required to have advanced mathematics knowledge such as matrix algebra or calculus. The author introduces machine learning algorithms, utilizing the widely used R language for statistical analysis. Each chapter includes examples, case studies, and interactive tutorials to enhance understanding. No prior programming knowledge is needed. The book leverages the tidymodels package, an extension of R, to streamline data p...
A.I. in 2020
This book collects the best articles about several artificial intelligence concepts that I have published online during 2020. It is dedicated to anyone interested in Artificial Intelligence and anyone who wants to understand some of the building blocks that form this fascinating technology. Here, you will find my best articles, updated and revisited, with some more insights, with a suitable format for book readers. The content of this book results from extensive research, long nights of studies, and some of my best years of work in the field in some prestigious enterprise companies in Europe. My goal is to share as much as possible through an affordable, simple, and straightforward language,...
Data Science For Dummies
Monetize your company’s data and data science expertise without spending a fortune on hiring independent strategy consultants to help What if there was one simple, clear process for ensuring that all your company’s data science projects achieve a high a return on investment? What if you could validate your ideas for future data science projects, and select the one idea that’s most prime for achieving profitability while also moving your company closer to its business vision? There is. Industry-acclaimed data science consultant, Lillian Pierson, shares her proprietary STAR Framework – A simple, proven process for leading profit-forming data science projects. Not sure what data science...
Strategists First
Ever wonder why you’ve had more strategy conversations than you can count, but not a single strategist discussion you can remember? That’s because strategy has made a career out of ignoring the strategist—until now. Strategists First will help you learn what every strategist needs to know, including: who strategists are, what strategists believe, how strategists behave, where strategists thrive, when strategists strike, and why strategists matter. If you’re an accomplished strategist, this book gives voice and visibility to your fight against the status quo. If you’re an aspiring strategist, this book delivers the beliefs and practices needed to live this identity into action.
The Decision Intelligence Handbook
Decision intelligence (DI) has been widely named as a top technology trend for several years, and Gartner reports that more than a third of large organizations are adopting it. Some even say that DI is the next step in the evolution of AI. Many software vendors offer DI solutions today, as they help organizations implement their evidence-based or data-driven decision strategies. But until now, there has been little practical guidance for organizations to formalize decision making and integrate their decisions with data. With this book, authors L. Y. Pratt and N. E. Malcolm fill this gap. They present a step-by-step method for integrating technology into decisions that bridge from actions to desired outcomes, with a focus on systems that act in an advisory, human-in-the-loop capacity to decision makers. This handbook addresses three widespread data-driven decision-making problems: How can decision makers use data and technology to ensure desired outcomes? How can technology teams communicate effectively with decision makers to maximize the return on their data and technology investments? How can organizational decision makers assess and improve their decisions over time?
97 Things About Ethics Everyone in Data Science Should Know
Most of the high-profile cases of real or perceived unethical activity in data science arenâ??t matters of bad intent. Rather, they occur because the ethics simply arenâ??t thought through well enough. Being ethical takes constant diligence, and in many situations identifying the right choice can be difficult. In this in-depth book, contributors from top companies in technology, finance, and other industries share experiences and lessons learned from collecting, managing, and analyzing data ethically. Data science professionals, managers, and tech leaders will gain a better understanding of ethics through powerful, real-world best practices. Articles include: Ethics Is Not a Binary Concept...
R Data Analysis without Programming
The new edition of this innovative book, R Data Analysis without Programming, prepares the readers to quickly analyze data and interpret statistical results using R. Professor Gerbing has developed lessR, a ground-breaking method in alleviating the challenges of R programming. The lessR extends R, removing the need for programming. This edition expands upon the first edition’s introduction to R through lessR, which enables the readers to learn how to organize data for analysis, read the data into R, and generate output without performing numerous functions and programming exercises first. With lessR, readers can select the necessary procedure and change the relevant variables with simple f...
Wanted: Human-AI Translators
Anyone who thinks artificial intelligence (AI) doesn’t really concern us yet is wrong. AI is no longer a matter for futuristic science fiction films or hip technology fairs. It is now everywhere in your life. That personalised advertising folder from the supermarket with offers “especially for you”? AI. The songs suggested for you on Spotify? AI. The self-regulating traffic lights on the corner of your street? AI. It is undeniable that AI is entering our lives at a dizzying speed. It is now part of our everyday world, and also penetrating business sectors everywhere. This raises many questions. What can AI do? What can it not do? To what extent is AI really intelligent and how far does...
Analytical Skills for AI and Data Science
While several market-leading companies have successfully transformed their business models by following data- and AI-driven paths, the vast majority have yet to reap the benefits. How can your business and analytics units gain a competitive advantage by capturing the full potential of this predictive revolution? This practical guide presents a battle-tested end-to-end method to help you translate business decisions into tractable prescriptive solutions using data and AI as fundamental inputs. Author Daniel Vaughan shows data scientists, analytics practitioners, and others interested in using AI to transform their businesses not only how to ask the right questions but also how to generate val...
African Artificial Intelligence
Artificial intelligence (AI) is upending life, work, and play as we know it, and it's only just getting started. The rise of AI is a milestone on par with the discovery of fire, the invention of the wheel, and the creation of the internet. In short, AI is going to change everything. For some, that's an exciting prospect. For others, it's terrifying. However you feel about AI, there's no escaping it, whether you're in a global metropolis or a farmer in rural KwaZulu-Natal. Dr Mark Nasila has been watching AI's ascent for over a decade, studying its effects on everything from agriculture and aviation to healthcare, education, entertainment, crime prevention, energy management, policy creation,...
