Machine Learning (ML) revolves around devising algorithms and models that identify concealed patterns. The learning process occurs through the generalization of many practical examples. ML systems efficacy increases with the accumulation of knowledge regarding the subjects under scrutiny. Applications of ML include image recognition, content recommendation, suspicious activity detection, and more

Deep Learning (DP) is a subset of ML that utilizes multi-layered neural networks, enabling autonomous identification of algorithms to address problems. High volumes of data are required to achieve efficiency and accuracy during the learning process.

Neural Networks (NN) are models inspired by the human brain’s functionality. Similar to neurons and synapses constituting the brain, numerous computational centers interconnect and interact within a neural network. Typically, NN possesses multiple layers, allowing parallel information processing. Neural networks are trained on labeled datasets with explicit patterns, after which the AI can process unlabeled data.

Natural Language Processing (NLP) is a discipline that emerged at the intersection of machine learning and mathematical linguistics. Natural language, the language utilized by humans, facilitates communication with AI systems. Language processing itself consists of comprehension and generation components. High-quality generation entails the capability to analyze and synthesize unstructured data from various sources.

Computer Vision (CV) is a branch of ML that endows information systems with the ability to “see” and extract data from visual images. Models automate tasks that rely on human vision, such as examining photographs, identifying their content, and interpreting the acquired information. Images and videos serve as educational materials. The primary objective is to collect, process, analyze, and comprehend digital images while extracting the data they encompass.