In our Previous article we have discussed about basic of machine learning, evaluation of machine learning and it’s types. In this article we will discuss about basic steps in machine learning.

General steps used in machine learning

There are 5 basic steps used to perform a machine learning task:

1) Collection data

Collect data from various sources like raw data, social media data, various emails etc.

this step (gathering past data) forms the foundation of the future learning. The better the variety, density and volume of relevant data, better the learning prospects for the machine becomes.

2) Preparing the data

In this step we need to clean any unwanted data,remove outliers, remove stopwords. Apply features selection and features engineering techniques to get best from data.

3) Training a model

This step involves choosing the appropriate algorithm and representation of data in the form of the model. The cleaned data is split into two parts – train and test (proportion depending on the prerequisites); the first part (training data) is used for developing the model. The second part (test data), is used as a reference.

4) Evaluating the model

To test the accuracy, the second part of the data (holdout / test data) is used. This step determines the precision in the choice of the algorithm based on the outcome. A better test to check accuracy of model is to see its performance on data which was not used at all during model build.

5) Improving the performance

This step might involve choosing a different model altogether or introducing more variables to augment the efficiency.

Refer ML for more details.

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