Data Science vs AI vs Machine Learning

 

Data Science vs Artificial Intelligence vs Machine Learning – How are they different?

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Data Science vs artificial intelligence vs Machine Learning is one of the most debated topics that often confuse tech enthusiasts. We hear this question being asked a lot, “What is the difference between data science artificial intelligence machine learning? A lot of the skills that are used for machine learning overlap a with data science, with a few differences but each has got its individual applications and are not the same. 

Data Science vs Artificial Intelligence vs Machine Learning – What is the difference?

Before we go on to understand the differences between data science artificial intelligence and machine learning , it is very important to understand what is data science and what is machine learning.

What is Data Science?

Data science is all about data, and I’m pretty sure you already knew that. But did you know that we use data science to make business decisions? I’m pretty sure you knew that as well. So what else is new here? Well, do you know how data science is used to make business decisions? No? Let’s look at that then.

We all know that every single tech company out there is collecting huge amounts of data. And data is revenue. Why is that? That’s because of data science. The more data you have, the more business insights you can generate. Using data science, you can uncover patterns in data that you didn’t even know existed. For example, you can discover that some guy who went to New York City for a vacation is most likely to splurge on a luxury trip to Venice in the next three weeks. That’s an example that I just made up, might not be true in the real world. But if you’re a company offering luxury tours to exotic destinations, you might be interested in getting this guy’s contact number.

Data science is being used extensively in such scenarios. Companies are using data science to build recommendation engines, and predicting user behaviour, and much more. All of this is only possible when you have enough amount of data so that various algorithms could be applied on that data to give you more accurate results.

There is also something called as prescriptive analytics in data science, which does pretty much the same predictions that we talked about in the rich tourist example above. But as an added benefit, prescriptive analytics will also tell you what kind of luxury tours to Venice a person might be interested in. For example, one person might want to fly first class but would be fine with a three star accommodation, whereas another person could be ready to fly economy but definitely needs the most luxurious stay and cultural experience. So even though both these people will be your rich clients, both of them have different requirements. So you can use prescriptive analytics for this.

You might be wondering, hey, that sounds a lot like artificial intelligence. And you’re not entirely wrong, actually. Because running these machine learning algorithms on huge datasets is again a part of data science. Machine learning is used in data science to make predictions and also to discover patterns in the data. This again sounds like we’re adding intelligence to our system. That must be artificial intelligence. Right? Let’s see.

What is Artificial intelligence?

Artificial intelligence, or AI for short, has been around since the mid 1950s. It’s not necessarily new. But it became super popular recently because of the advancements in processing capabilities. Back in the 1900s, there just wasn’t the necessary computing power to realise AI. Today, we have some of the fastest computers the world has ever seen. And the algorithm implementations have improved so much that we can run them on commodity hardware, even your laptop or smartphone that you’re using to read this right now. And given the seemingly endless possibilities of AI, everybody wants a piece of it.

But what exactly is artificial intelligence? Artificial intelligence is the ability that can be imparted to computers which enables these machines to understand data, learn from the data, and make decisions based on patterns hidden in the data, or inferences that could otherwise be very difficult (to almost impossible) for humans to make manually. AI also enables machines to adjust their “knowledge” based on new inputs that were not part of the data used for training these machines.

Another way of defining AI is that it’s a collection of mathematical algorithms that make computers understand relationships between different types and pieces of data such that this knowledge of connections could be utilised to come to conclusions or make decisions that could be accurate to a very high degree.

But there’s one thing you need to make sure, that you have enough data for AI to learn from. If you have a very small data lake that you’re using to train your AI model, the accuracy of the prediction or decision could be low. So more the data, better is the training of the AI model, and more accurate will be the outcome. Depending on the size of your training data, you can choose various algorithms for your model. This is where machine learning and deep learning start to show up.

In the early days of AI, neural networks were all the rage. There were multiple groups of people across the globe working on bettering their neural networks. But as I mentioned earlier in the post, the limitations of the computing hardware kind of hindered the advancement of AI. But from the late 1980s all the way up to the 2010s, machine learning it was. Every major tech company was investing heavily in machine learning. Companies such as Google, Amazon, IBM, Facebook, etc. were virtually dragging AI and ML PhD. people straight from universities. But these days, even machine learning has taken a back seat. It’s all about deep learning now. There’s definitely been an evolution of AI in the last few decades, and it’s getting better with every passing year. You can visualise this evolution from the image below.

What is Machine Learning?

Arthur Samuel coined the term Machine Learning in the year 1959. He was a pioneer in Artificial Intelligence and computer gaming, and defined Machine Learning as “Field of study that gives computers the capability to learn without being explicitly programmed”.

Let’s talk about machine learning now. Machine Learning (ML) is considered a sub-set of AI. You can even say that ML is an implementation of AI. So whenever you think AI, you can think of applying ML there. As the name makes it pretty clear, ML is used in situations where we want the machine to learn from the huge amounts of data we give it, and then apply that knowledge on new pieces of data that streams into the system. But how does a machine learn, you might ask.

There are different ways of making a machine learn. Different methods of machine learning are supervised learning, non-supervised learning, semi-supervised learning, and reinforced machine learning. In some of these methods, a user tells the machine what are the features or independent variables (input) and which is the dependent variable (output). So the machine learns the relationship between the independent and dependent variables present in the data that is provided to the machine. This data which is provided is called the training set. And once the learning phase or the training is complete, the machine, or the ML model, is tested on a piece of data which the model has not encountered before. This new dataset is called the test dataset. There are different ways in which you can split your existing dataset between the training and the test dataset. Once the model is mature enough to give reliable and high accuracy results, the model will be deployed to a production setup where it will be used against absolutely new datasets for problems such as predictions or classification.

There are various algorithms in ML which could be used for prediction problems, classification problems, regression problems, and more. You might have heard of algorithms such as simple linear regression, polynomial regression, support vector regression, decision tree regression, random forest regression, K-nearest neighbours, and the like. These are some of the common regression and clustering algorithms used in ML. There are many more as well. And there are a lot of data preparation or pre-processing steps you need to take care of even before training your model. But ML libraries such as SciKit Learn have evolved so much that even an app developer without any background in mathematics or statistics, or even a formal AI education, can start using these libraries to build, train, test, deploy, and use ML models in the real world. But it always helps to know how these algorithms work, so that you can make informed decisions when you are to select an algorithm for your problem statement. With this knowledge of ML, let’s talk a bit about deep learning now.