Data Science vs Machine Learning - Part 1
While reading about artificial intelligence, you might have heard terminologies such as machine learning or data science or neural networks or deep learning. What do these terms mean?
Let's say you have a housing dataset with the size of the house, number of bedrooms, number of bathrooms, whether the house is newly renovated as well as the price it is listed at. If you want to build a mobile app to help people price houses, so these parameters would be the input A, and price would be the output B. Then, this would be a machine-learning system, and in particular would be one of those machine learning systems that learns inputs to outputs, or A to B mappings.
Machine learning often results in running an AI system. It is a piece of software that you can input A, these properties of house anytime and it will output B, the price automatically. So, if you have an AI system running, serving dozens or hundreds of thousands of millions of users, that's usually a machine-learning system.
In contrast, here's something else you might want to do, which is to have a team analyze your dataset in order to gain insights. So, a team might come up with a conclusion like, "Hey, did you know if you have two houses of a similar size, they've a similar square footage, if the house has three bedrooms, then they cost a lot more than the house of two bedrooms, even if the square footage for both is same."
Or, "Did you know that newly renovated homes have a 15% premium, and this can help you make decisions such as, given a similar square footage, do you want to build a two bedroom or three bedroom size in order to maximize value? "
Or, "Is it worth an investment to renovate a home in the hope that the renovation increases the price you can sell a house for?"
These would be examples of data science projects, where the output of a data science project is a set of insights that can help you make business decisions, such as what type of house to build or whether to invest in renovation.
The boundaries between these two terms, machine learning and data science are actually little bit ambiguous, and these terms are not used consistently even in industry today. But what I'm giving here is maybe the most commonly used definitions of these terms, but you will not find universal adherence to these definitions.
To formalize these two notions a bit more, machine learning is the field of study that gives computers the ability to learn without being explicitly programmed. So, a machine learning project will often result in a piece of software that runs, that outputs B given A. In contrast, data science is the science of extracting knowledge and insights from data. So, the output of a data science project is often a slide deck, the PowerPoint presentation that summarizes conclusions for executives to take business actions or that summarizes conclusions for a product team to decide how to improve a website.
Let me give an example of machine learning versus data science in the online advertising industry. Today, to launch any platform, we have a piece of AI that quickly tells us what's the ad you are most likely to click on. So, that's a machine learning system. This turns out to be incredibly lucrative AI system to input information about you and about the ad and it outputs whether you click on this or not.
In the next part, we will learn about deep learning and neural networks and how they are used in machine learning and artificial intelligence.