Data Science with Machine Learning

By ridhigrg |Email | Mar 25, 2020 | 3336 Views


What is Machine Learning?
Machine learning is the use of AI to help machines make predictions based on previous experience. We can say that ML is a subset of AI. The quality and authenticity of the data is representative of your model. The outcome of this step represents the data that will be used for the purpose of training.

After the assembling of data, it's prepared to train the machines. Afterward, filters are used to eliminate errors and handle the missing data type conversions, normalization, and missing values.

For measuring the objective performance of a certain model, it's a good idea to use a combo of different metrics. Then you can compare the model with the past data for testing purposes.

For performance improvement, you have to tune the model parameters. Afterward, the tested data is used to predict the model performance in the real world. This is the reason many industries hire the services of machine learning professionals for developing ML-based apps.

What is Data Science?

Data Science Process
For defining the data science process, we can say that there are different dimensions of data collection. They include data collection, modeling, analysis, problem-solving, decision support, designing of data collection, analysis process, data exploration, imagining and communicating the results, and giving answers to questions.


Machine Learning relies heavily on available data. Therefore, they have a strong relationship with each other. So, we can say that both the terms are related.


However, it is used to structure data, look for compelling patterns and advise decision-makers so that they can revolutionize business needs.

Source: HOB