11th Global Summit on Artificial Intelligence and Neural Networks

  Natural language processing helps computers to speak with humans in their native language while also automating other language-related processes. NLP, for example, enables computers to read text, hear voice, analyse it, gauge sentiment, and identify which parts are significant. Machines can now analyse more language-based data than humans, without becoming fatigued and in a consistent, unbiased manner. Given the massive volume of unstructured data generated every day, from medical records to social media, automation will be essential for efficiently analysing text and audio data. Natural language processing encompasses a wide range of techniques for analysing human language, including statistical and machine learning methods, as well as rule-based and algorithmic approaches. We require a diverse set of techniques since text- and voice-based data, as well as actual applications, vary greatly. Tokenization and parsing, lemmatization/stemming, part-of-speech tagging, language detect

  Deep learning is a subsection of machine learning and Artificial Intelligence (AI) that uses advancements in technology to allow computers to acquire data, knowledge, and experiences in the same way that people do. Deep learning relies heavily on statistical and forecasting models, both of which are components of data science. Deep learning has numerous advantages for data scientists who are in charge of gathering, analyzing, and interpreting large amounts of data. Deep learning makes this procedure simpler and more efficient. At its most basic, deep learning can be viewed as a method of automating predictive analyses. Deep learning algorithms are stacked on top of one another in a hierarchy of growing complexity and abstraction, whereas traditional machine learning algorithms are linear. Deep learning uses supervised learning in situations such as image classification or object detection, as the network is used to predict a label or a number (the input and the output are both know

  Machine learning (ML) is a subset of artificial intelligence (AI) that enables software applications to become more accurate at predicting outcomes without expressly programming them to do so. Machine learning algorithms anticipate new output values by using historical data as input. Machine learning is essential because it provides businesses with insights into trends in customer behavior and business operational patterns, as well as assisting in the development of new products. Machine learning is central to the operations of many of today's leading businesses, including Facebook, Google, and Uber. For many businesses, machine learning has become a major competitive differentiator. Types of machine learning: Supervised learning Unsupervised learning Semi-supervised learning Reinforcement learning Advantages and disadvantages of machine learning: Machine learning has been used in a variety of applications, from predicting customer behavior to developing the operating system f