Machine Learning is a way to apply AI to systems, allowing them to learn and improve automatically from experiences, eliminating the need for explicit programming. It revolves around the development of computer programs capable of accessing data and independently learning from it. The primary objective is to enable computers to learn and make decisions without human intervention, thereby adapting their actions accordingly. In today's business landscape, this technique is pivotal for any company or enterprise striving for digital transformation.
Recent studies reveal that 29% of global developers have actively worked on AI/Machine Learning software in the past year. It's projected that 25% of Fortune 500 companies will incorporate essential AI components, such as text analysis and machine learning, into their robotic process automation (RPA) endeavors, paving the way for new applications of intelligent process automation (IPA). Additionally, analysts predict that by 2021, about 15% of customer experience applications will offer hyper-personalized services, combining diverse data sources and reinforcement learning algorithms.
The foundation of Machine Learning rests upon the confidence in systems' ability to learn from data, discern patterns, and make decisions with minimal human intervention. However, the journey begins with algorithm training, a critical aspect of the Machine Learning process. This process, for example, is integral in developing virtual assistants or chatbots, commonly found in advanced e-commerce websites.
These chatbots also use Natural Language Processing (NLP) techniques to dissect, comprehend, and interpret human language, facilitating the annotation processes. These techniques also extend to text recognition, enabling the extraction and analysis of information from image-based documents.