The three stages of building a machine learning model are:

Model Building

Choose a suitable algorithm for the model and train it according to the requirement

The six steps to building a machine learning model include: 

1. Contextualise machine learning in your organisation 
2. Explore the data and choose the type of algorithm  
3. Prepare and clean the dataset 
4. Split the prepared dataset and perform cross validation  
5. Perform machine learning optimisation 
6. Deploy the model 

Model Testing

In machine learning, model testing is referred to as the process where the performance of a fully trained model is evaluated on a testing set. The testing set consisting of a set of testing samples should be separated from the both training and validation sets, but it should follow the same probability distribution as the training set.

Applying the Model

Make the required changes after testing and use the final model for real-time projects