Quantum Trading
Recognizing consistent elements within data using machine learning tools. The go-to programming language for this? Python.
Before we dive into analysis, data must be cleaned and prepped, ensuring it’s in a format machines can decipher.
Here, algorithms label data based on set features, making it a part of supervised learning.
Algorithms sort data into groups based on similar features, a prime example of unsupervised learning.
Through regression, algorithms pinpoint relationships between variables to make predictions based on known data. Another facet of supervised learning.
Grounded in history, it relies on past data, learning by example to make future predictions.
Here, we break down patterns into simpler sub-patterns called primitives, connecting these building blocks to form a full pattern.
Enter the realm of artificial neural networks, adaptable entities that decode complex input-output relations.
