Trading and Machine Learning

Photo by Possessed Photography on Unsplash

Machine learning is a subset of artificial intelligence (AI) that involves developing algorithms and models that allow computer systems to learn and improve from data without being explicitly programmed.

In traditional programming, a programmer writes code to perform a specific task. However, in machine learning, the computer learns how to perform a task by analyzing large amounts of data and identifying patterns and relationships within the data. This allows the system to improve its performance over time as it is exposed to more data and feedback.

There are three main types of machine learning:

• Supervised learning — the algorithm is trained on labeled data, where the desired output is known and learns to predict the production for new, unlabeled data.
• Unsupervised learning — the algorithm is trained on unlabeled data and learns to find patterns or relationships within the data.
• Reinforcement learning — the algorithm learns through trial and error by receiving feedback as rewards or penalties for its actions.

Machine learning is used in various applications, such as image and speech recognition, natural language processing, recommendation systems, fraud detection, and autonomous vehicles, among others.