Machine Learning Process

In the machine learning workflow, we use historical data to start exploring and discovering the relationships that exist between the input features and the target. In this article, I’ll walk you through the complete process of Machine Learning.

The Process of Machine Learning

The goal of machine learning is to discover patterns and relationships in data and put those findings to use. This process of discovery is achieved through the use of modelling techniques that have been developed over the past 30 years in statistics, computer science and applied mathematics.

These different approaches can range from simple to extremely complex, but they all share a common goal: to estimate the functional relationship between the input characteristics and the target variable.

These machine learning approaches also share a common process, as depicted in the image below. First, it uses historical data to build and optimize a model which is, in turn, used to make predictions based on new data.

As shown in the image above, the machine learning process usually begins with collecting historical data. Then, this data is prepared to fit into a machine learning model. Then the next step is to build the model, here we are using machine learning algorithms.

The next step is to evaluate the model. Typically, this step uses the test set obtained after dividing the historical data into training and testing sets. Then the next step is model optimization, which usually means turning the hyperparameters. A common machine learning technique for tuning hyperparameters is the use of the grid search algorithm.

Then the last step is to test the model on a new invisible dataset and re-evaluate the model if it is not performing well.

You can learn each about each step in the workflow of Machine Learning from below:

I hope you liked this article on the complete process of Machine Learning. Feel free to ask your valuable questions in the comments section below.