Machine learning ML process

The machine learning (ML) process involves several key steps, including data collection, feature extraction, model selection, model training, model evaluation, and deployment. The first step is to collect and preprocess the data, ensuring that it is of high quality and relevant to the task at hand. Next, relevant features are extracted from the data and used to train a model, which is then evaluated and refined as needed. Once the model is deemed accurate and reliable, it can be deployed to make predictions or automate decision-making.