Machine Learning Techniques Application: Social Media, Agriculture, and Scheduling in Distributed Systems

Introduction

Machine learning (ML) is a discipline of information technology that gives machines the ability to “learn” with data, without being expressly programmed, this means in other words that these programs change their behavior by learning from data. Machine learning is the future of computing. From home automation to landing a rover on a comet, everything is digitalized, and these are made possible only because of intelligent systems that use Machine learning and their associated applications (Wernick, Yang, Brankov, Yourganov, & Strother, 2010). With advances in ML, probabilistic standards and deep neural network machine can now promptly convert spoken conversion and written, identify and precisely caption photos, recognize faces and be a personal assistant for the consumers. The latest inventions in Artificial Intelligence (AI) are the result of core advances in AI, including improvements in ML, and perception, on a stage set by improvements in multiple fields of computer science. Processing power has raised and has compared to cloud computing. For the meantime, the extension of the web has produced chances to collect, store and share massive volumes of data (Hatcher & Yu, 2018). There also have been prominent strides in probabilistic modeling, in which processing system examine probabilities and make the best solution or suggestion, and ML in which a computer gets better at something based on the data that it collects. This process proves that the future of machine learning is heading in many ways making the lives of millions of people more comfortable, productive, and effective. Figure 1 depicts the Application of various Machine Learning Techniques.