Use Cases

Here you’ll find everything about AI use cases, including Python tutorials on implementing specific use cases.

A machine learning use case is a specific application or instance of using algorithms and techniques to solve a problem or accomplish a task. It typically involves a combination of data preparation, model selection, training, and deployment. AI and machine learning are closely related. In fact, machine learning is a subset of AI, which refers to the use of algorithms and statistical models to enable a system to improve and learn from data over time. In other words, machine learning algorithms allow AI systems to automatically learn and improve from experience, without being explicitly programmed. AI can be applied in a wide range of industries and applications. Some common examples of AI in action include virtual assistants like Siri and Alexa, which use natural language processing to understand and respond to user requests. Another example is self-driving cars. They use a combination of computer vision and machine learning algorithms to navigate roads and traffic. Additionally, AI is often used in healthcare, finance, and other industries. In all these industries, AI improves decision-making, automate processes, and enhance the customer experience.