Differentiating Between AI, Machine Learning, and Data Science

It is easy to mix up terms like data science, machine learning, and artificial intelligence. To a layman, the terms might seem similar and too complex to discern.

Mixing up these terms could lead to the wrong perception of the technologies that represent them and how to use them. Here are some essential clarifications:

●      Artificial intelligence simulates the functioning of a human brain by machines. This is done by creating artificial neural networks that display properties similar to human intelligence. Aside from having learning, reasoning, and self-correction as its main function, artificial intelligence is an entire scientific field. It has numerous applications and covers a wide range of technologies.

●      Machine learning is a term that describes a computer system that doesn’t need specific programming but, instead, learns and improves itself from the environment. It first gathers information and then uses algorithms to create valuable insights and predictions based on data analysis.

●      Data science uses data sets to extract information. It employs computer programming, data visualization and engineering, uncertainty modeling, mathematics, pattern recognition, machine learning, and many other techniques. It is also used in AI and machine learning to drive and analyze data and help resolve problems and gain insights.

As you can see, the three terms describe different technologies. AI is still in evolution, while data science and machine learning have a bit more substantial use currently. These technologies are also connected and mutually applied to solutions to resolve different problems.

For example, machine learning uses data science to interpret information and make predictions based on that information.

I hope this post helped you understand the difference between AI, ML, and data science a little bit better. Have more questions? Feel free to comment and ask!