The machine learning process
Machine learning is a broad field that encompasses various approaches to training models. Each machine learning approach has its own process for developing and optimizing models.
In unsupervised learning, the model learns from unlabeled data to discover hidden patterns or structures. The process typically involves data preprocessing, model training, model evaluation, and model tuning.
Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment. The process involves environment setup, agent training, policy evaluation, and policy improvement.
For supervised learning and transfer learning, establishing a reliable machine learning model involves carefully progressing through three essential stages: training, validation, and testing. These stages represent a structured approach to bringing a model to life, optimizing its performance, and ensuring its readiness for real-world application...
