Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library.

Abstract: Deep Learning has enjoyed an impressive growth over the past few years in fields ranging from visual recognition to natural language processing. Improvements in these areas have been fundamental to the development of self-driving cars, machine translation, and healthcare applications. This progress has arguably been made possible by a combination of increases in computing power and clever heuristics, raising puzzling questions that lack full theoretical understanding. Here, we will discuss the relationship between the theory behind deep learning and its application. This panel discussion will be hosted remotely via Zoom.

If you are interested in learning Data Science with R, but not interested in spending money on books, you are definitely in a very good space. There are a number of fantastic R/Data Science books and resources available online for free from top most creators and scientists. Here are such 13 free 20 free (so […]

With the proliferation of information technologies and data among us, cybersecurity has become a necessity. Machine learning helps organisations by getting insights from raw data, predicting future outcomes and more. For a few years now, such utilisation of machine learning techniques has been started being implemented in cybersecurity. It helps in several ways, including identifying frauds, malicious codes and other such. In this article, we list down the top eight books, in no particular order, on machine learning In cybersecurity that one must-read.

This issue of AI Magazine brings together a collection of articles on challenges, mechanisms, and research progress in turn-taking and coordination between humans and machines. The contributing authors work in interrelated fields of spoken dialog systems, intelligent virtual agents, human-computer interaction, human-robot interaction, and semiautonomous collaborative systems and explore core concepts in coordinating speech and actions with virtual agents, robots, and other autonomous systems. Several of the contributors participated in the AAAI Spring Symposium on Turn-Taking and Coordination in Human-Machine Interaction, held in March 2015, and several articles in this issue are extensions of work presented at that symposium. The articles in the collection address key modeling, methodological, and computational challenges in achieving effective coordination with machines, propose solutions that overcome these challenges under sensory, cognitive, and resource restrictions, and illustrate how such solutions can facilitate coordination across diverse and challenging domains.

We've teamed up with O'Reilly for our newest bundle! Get ebooks like Learning SQL, 3rd Edition, Building Machine Learning Powered Applications, and Generative Deep Learning. Plus, your purchase will support Code For America! Normally, the total cost for the ebooks in this bundle is as much as $798. Here at Humble Bundle, you choose the price and increase your contribution to upgrade your bundle! This bundle has a minimum $1 purchase.

Description: This book provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics, medicine, computer science, and information theory. The authors present the material in an accessible style and motivate concepts using real-world examples. Be prepared, it is a big book!. Also, check out their great probability cheat sheet here.

Artificial intelligence and machine learning are fast moving out of the hype status and into the reality status. South African industry recently launched the AI Institute of South Africa, with the objective of consulting with government on AI policy, while also representing South Africa in global AI forums and initiatives. The AI world is faced with many questions. How can we ensure that AI is for good and will not destroy itself and in the process, the world? Do we need global regulations?

"This book covers the building blocks of the most common methods in machine learning. This set of methods is like a toolbox for machine learning engineers. Those entering the field of machine learning should feel comfortable with this toolbox so they have the right tool for a variety of tasks. In other words, each chapter focuses on a single tool within the ML toolbox […]. This book is for readers looking to learn new machine learning algorithms or understand algorithms at a deeper level. Specifically, it is intended for readers interested in seeing machine learning algorithms derived from start to finish".

Aside from staying alive and healthy, the biggest concern most people have during the pandemic is the future of their jobs. Unemployment in the U.S. has skyrocketed, from 5.8 million in February 2020 to 16.3 million in July 2020, according to the U.S. Bureau of Labor Statistics. But it's not only the lost jobs that are reshaping work in the wake of COVID-19; the nature of many of the remaining jobs has changed, as remote work becomes the norm. And in the midst of it all, automation has become potentially a threat to some workers and a salvation to others. In our upcoming special issue, titled "Automation and jobs in the new normal," we examine this tension and explore the good, bad, and unknown of how automation could affect jobs in the immediate and near future.