In the age of data-driven technologies, it’s very common for you to use terms like data science, artificial intelligence, machine learning, and deep learning. But what these terms mean and how they relate or differ from each other. In this article, I’ll walk you through the difference between data science, artificial intelligence, machine learning, and deep learning.
Difference Between Data Science, Artificial Intelligence, Machine Learning and Deep Learning
I’ll start to differentiate between data science, artificial intelligence, machine learning, and deep learning by explaining all of these terms one by one instead of putting them in a tubular comparison as they are somehow completely different from each other and all of these terms are also very dependent on each other.
So it’s better to make a difference between data science, artificial intelligence, machine learning and deep learning one by one instead of putting them in a tabular representation.
Data science is about the data used to make business decisions. Today, companies are collecting a huge amount of data which has introduced terms like cloud storage and big data. It is now believed that the more data you have, the more patterns you can make, which at the end of the day will eventually help in decision making.
Using data science, a business can easily unlock even those models that didn’t even exist before. Such deep models cannot be judged by experienced managers, so this is where the data comes in handy. Today, data science is used from predicting a user’s buying habits to predicting their vote in the next presidential election.
Now you might be thinking that it sounds a lot like artificial intelligence. I can only say that you are not completely wrong. Because running predictive machine learning algorithms on huge data sets is also part of data science.
Besides, machine learning is used in data science to make predictions and also to discover patterns from the huge amount of data. It also seems like we’re using artificial intelligence, doesn’t it? Now let’s see what artificial intelligence is and how it is different.
Artificial intelligence isn’t new, it’s been around since the 1950s. It sounds like a new concept due to the increased computing power of the hardware we use today. Previously, we didn’t know much about artificial intelligence because we didn’t have the computing power to use it, so it was just kind of a theoretical concept in universities.
But now that we’ve discovered some of the most powerful operating systems, processors, and hardware, we can harness the power of artificial intelligence in our systems. Not only powerful computer systems, but we can also now even use the power of artificial intelligence in laptops and smartphones.
So what is artificial intelligence? Artificial intelligence is the power of computers that enables machines to understand and learn from patterns hidden in data to make such decisions that are manually impossible for humans.
Put simply, artificial intelligence is the collection of those powerful mathematical algorithms that can be used to find relationships in the huge amount of data which can be used to make precise decisions for the future.
So what is Machine Learning? Machine Learning and Artificial Intelligence are strongly related to each other. In the previous section, I explained what is artificial intelligence. So how AI does what it does? By using the mathematical algorithms right? So when we feed those mathematical algorithms to train a model that learns from data then this process is known as Machine Learning.
And when we are using the trained model to make predictions or decisions in real-time then this is known as Artificial Intelligence. There are many categories of Machine Learning the popular ones are Supervised and Unsupervised. Also, there are many types of algorithms in Machine Learning, the popular ones are clustering, regression and classification.
Machine Learning is not only about training models, as the models are trained by using a huge amount of data and that data first need to go through some processing like the process of feature selection and data preparation to fit into the machine learning algorithms.
Deep Learning is an advanced version of Machine Learning. It’s not that machine learning is failing somewhere, it’s just that sometimes the data is very huge and has too many features. While these features are among those that will affect the accuracy of your model if you remove them, there is no point in using machine learning algorithms.
In these cases, we need to go beyond machine learning algorithms such as regression, clustering, and classification. This is where Deep Learning comes in. Deep Learning consists of creating neural networks. The other difference between machine learning and deep learning is that deep learning needs much more powerful hardware than machine learning.
Deep Learning requires the use of most powerful hardware systems so mostly GPUs are used for training Deep Learning Models.
So, I hope you are now able to differentiate between Data Science, Artificial Intelligence, Machine Learning and Deep Learning. I hope you liked this article on the difference between Data Science, Artificial Intelligence, Machine Learning and Deep Learning. Feel free to ask your valuable questions in the comments section below.