Are you among the bunch of people who are still trying to differentiate between the buzzwords Artificial intelligence, Machine learning, Deep learning and Data Science. I am sure most of you have heard these terminologies but never had got a chance to clear the confusion. I promise this article going to do the clear the air for you.

Moving forward, all these four terminologies are usually used interchangeably, in reality they do not quite refer the same things. As you can see from the below image of four concentric circles, Deep learning is a subset of Machine learning, which is also a subset of Artificial intelligence and Data Science is a common subset of all the three terms. So, Artificial intelligence is the all-inclusive concept that initially developed, then followed by Machine learning that emerged later, and lastly Deep learning that is promising to keep up and broaden the horizon of the advances of Artificial intelligence to another level.

Figure 1: Understanding the intersections of buzzwords

Not getting a clear picture yet? Okay, So Let’s dig deeper so that you can understand which is better for your specific use case: artificial intelligence, machine learning, data science or deep learning.

Figure 2: Different types of Buzzwords

Data Science

Data Science is the term coined for the entire set of tools and techniques used to analyse data and gather insights from it. It uses scientific methods, processes, and algorithms. Essentially, the aim is to discover hidden trends and patterns in raw data to help businesses grow and achieve its goal. The term became popular and gathered everyone’s attention in 2012, when Harvard Business Review called it “The Sexiest Job of the 21st Century”. The various phases in the data science field are

· Defining the Business Objective

· Data gathering and preparation

· Exploratory Data Analysis

· Model building

· Evaluating the Model

· Providing Conclusion

Machine Learning

Machine learning is the tool to analyse, understand and identify pattern in the data. The key idea behind machine learning is that the a model or a machine can be trained to automate tasks that would be exhaustive or time consuming for a human. Machine Learning requires minimal human intervention to take decisions.

Machine learning uses input data feed to an algorithm that can understand and compose the relationship between the input and the output into model (i.e.) equation. When the machine completes learning, it can predict the value or the category of unseen data point.

#data-analysis #machine-learning #data-science #deep-learning #artificial-intelligence