Before building a model, we define the problem we want to solve. This involves specifying the task we want to perform (e.g., classification, regression, clustering, etc.) and the type of data we will be working with.

Once we have defined the problem, we collect the data we will use to train and test our model. This may involve scraping data from the web, querying a database, or using pre-existing datasets. Once we have collected the data, we will preprocess the data by cleaning, transforming, and splitting it into training, validation and testing sets.

There are many different machine learning algorithm to choose from, each with its own strengths and weaknesses. Our choice of model will depend on the type of problem we are trying to solve, the type of data we are working with, and our own preferences and expertise.

Once we have chosen the algorithm, we then train it on our training data. This involves feeding the model input data, allowing it to make predictions, and adjusting its parameters to minimize the loss function, the difference between its predictions and the actual outputs.

Once the model has been trained, we evaluate its performance on our testing data. This involves using various metrics to assess the model's accuracy, precision, recall, and other performance characteristics.

If the model's performance is not satisfactory, we make adjustments to the model's architecture, its hyperparameters, or its training procedure. We may also need to collect more data, preprocess the data differently, or try a different model altogether.

Once we are satisfied with the model's performance, we proceed to deploying the model to a production environment, where it can be used to make predictions on new & unseen data.