Machine Learning Workflow





Machine Learning how to works in a step by step process with diagram:

Here Historic data is collected the entire data and used as the input for machine learning. This may however used indirectly it may need to be cleared and transformed etc. Some of the common tasks during feature engineering include handling missing values, handling outliers creating new features out of existing ones etc.

After feature engineering the data is split into two types are train data and data. The train data is used for training the machine learning model and data information. In other words, the machine learns from the training data. To build the model machine learning some algorithms are used.

Once the model is built it validated against test data. The concept of the test data is that it is real-time data. If the model performance on both train and test data satisfactory then the model completely.

Example:

Spam classification in Email conversations:

Historic data :

In emails msgs are received by user containing labels whether the user marked it as Spam or not

Feature Engineering :

Extracting information from past emails such as sender mail ID, sender IP, characteristics of email.

Train and Test data :

Some of the emails are kept for a test, the remaining are used to train the machine learning model.

Machine Learning algorithm :

Supervised learning algorithms to learn the pattern in train data. The patterns are a general representation of what makes email spam or not spam

Machine learning Model :

The model built using the train data



Model Validation :

Test the machine learning model against the emails in the best data in order to check if the model is able to predict spam emails as spam correctly.

New data :

New email being sent to the user in real – time

Results :

If the models detect that this new email is spam, then the email is moved the “Spam” folder otherwise it is related in the user’s inbox.

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