The machine learning process is an iterative process. It cannot be completed in one go. The most important activities to be performed for a machine learning solution are as follows:
- Define the machine learning problem (it must be well-defined).
- Gather, prepare, and enhance the data that is required.
- Use that data to build a model. This step goes in a loop and covers the following substeps. At times, it may also lead to revisiting Step 2 on data or even require the redefinition of the problem statement:
- Select the appropriate model/machine learning algorithm
- Train the machine learning algorithm on the training data and build the model
- Test the model
- Evaluate the results
- Continue this phase until the evaluation result is satisfactory and finalize the model
- Use the finalized model to make future predictions for the problem statement.
There are four major steps involved in the whole process, which is iterative and repetitive, till the objective is met. Let's get into the details of each step in the following sections. The following diagram will give a quick overview of the entire process, so it is easy to go into the details:
