The basic steps of CNC machine learning typically include the following:

1. Data collection: Collect data from CNC machines, such as machine status, performance, and maintenance records.
2. Data preprocessing: Clean and prepare the data for analysis, such as removing outliers, missing values, and irrelevant information.
3. Feature selection: Identify and select relevant features that will be used as inputs to the machine learning model.
4. Model selection: Choose an appropriate machine learning algorithm and model architecture that can effectively learn from the data.
5. Model training: Train the machine learning model on the collected data, using techniques such as supervised, unsupervised or reinforcement learning.
6. Model evaluation: Evaluate the performance of the trained model using metrics such as accuracy, precision, recall, and F1-score.
7. Model deployment: Deploy the trained model to a CNC machine or system, and use it to make predictions or control decisions.
8. Model monitoring: Regularly monitor the performance of the deployed model and update it as necessary.

It’s important to note that the steps may vary depending on the specific application and goal of the CNC machine learning project.