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Big data analytics is one high focus of data science and there is no doubt that big data is now quickly growing in all science and engineering fields. Big data analytics is the process of examining and analyzing massive and varied data that can help organizations make more-informed business decisions, especially for uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information. Big data has become essential as numerous organizations deal with massive amounts of specific information, which can contain useful information about problems such as national intelligence, cybersecurity, biology, fraud detection, marketing, astronomy, and medical informatics. Several promising machine learning techniques can be used for big data analytics including representation learning, deep learning, distributed and parallel learning, transfer learning, active learning, and kernel-based learning. In addition, big data analytics demands new and sophisticated algorithms based on machine learning techniques to treat data in real-time with high accuracy and productivity.

The papers published in this Special Issue (Machine Learning Technologies for Big Data Analytics) have covered various vital topics enriching the state of the art in artificial intelligence, machine learning, and big data analytics. Additionally, these search papers build upon the fundamental techniques and approaches accomplished earlier. The creativity in the established papers resides in the methods, reviews, and experimental techniques that present an outstanding value for beneficial applications. That presents one of the explanations for why this Special Issue has been named “Machine Learning Technologies for Big Data Analytics”. Nevertheless, there is another explanation: practical applications need researchers, scientists, and engineers to find solutions for the big data problems consistent with current technologies and react to the demands from the near future. That is why the searchers must utilize and develop artificial intelligence and machine learning methods for a specific need. The reader will see this Special Issue as valuable for that goal.