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Difference between Machine Learning, Data Science, AI, and Deep Learning
We are so encompassed with technology all around us that we practice it often without knowing its original meaning or why it is here in the first place. Developers, programmers, and internet technicians might have been using those technologies without ever knowing the original meaning that these carry. Deep learning, machine learning, artificial intelligence, and data science are the names of such technologies that are often too confused with each other that their original meaning is completely or partially lost.
Not only this confusion exists but the professionals or simple users dealing with this don't seem to be working on it for the eradicating of this confusion. If you have simply landed on this article for the sake of eradicating this confusion then you have come to the right place. Following is a detailed narrative on these terms, cleared for you with a dedicated example for each;
What is artificial intelligence?
Artificial intelligence is a complex narration or more like a type of programming fed to the computers and technological systems with which they can develop a simulated intelligence not as great as for humans but fine enough to pull off some complicated tasks. Artificial intelligence simply enough means that such capability is being fed to the computers and digital systems in which they can not only think, learn but also understand things like humans. It is not an easy concept to apprehend but it is what it is in a nutshell.
Other than that many people believe that it is like giving away the power of reasoning and intelligence to the robots so they can act like humans and someday rule the earth. Well, the ignition depicted in this line is true this is pretty much the goal with Artificial intelligence so that the computers can perceive things like humans but ruling the earth is pretty far-fetched here.
Artificial intelligence related systems work in a specific way in which they can receive or extract meaningful insight from a pile of data and manipulate the environment with it.
Example of AI
Suppose you could advise others about specific music based on the fact that you know their taste in the music and what they love to hear. The same goes for the AI, on the internet you might have seen the websites, players on which you hear music or stores from where you buy products making suggestions to you. Where do you think those suggestions come from? AI systems are actively working on the input data provided by the user in order to extract information out of it and funneling it back to you, pretty amazing, right?
What is machine learning?
Machine learning can be understood or perceived as a subset of artificial intelligence where AI is a vast subject to cover. Machine learning based technologies focus on extracting insight from the data they are being fed with and in turn teaching the computers and technological systems to learn and make predictions using this data. The programming is not required here as it follows a more direct approach in which the data is being interpreted in real-time and the insight it carries is being taught to the machines for making predictions about certain something. Stocks, statistics, and tech-oriented decisions normally circulate around the prospects of machine learning.
Examples of the ML
Seeing at the dashboard of a certain website, tool or software can be a superb example of machine learning. As you are about to commence the website or tool into performing a dedicated action, the systems know the action in advance and allow you to be quickly done with it. Continuous feeding of data and using the dashboard will automatically create shortcuts or hotkeys for you to consider when pulling off the same action. How it was able to do that? Normally learning from your behavior and the types of activities you perform using the dashboard and simply categorizing everything for your ease.
What is Deep learning?
Deep learning is a subset of machine learning. Deep in the sense that it involves a variety of steps for ambient processing of data. Machine learning relies on the algorithms that use linear transformations to produce output on the data. But in case of deep learning non-linear transformations are done to obtain the output, thus going a step forward to obtain each and every insight the data has to offer whether it could be making predictions, making a process easier than it ever is or sharing something new with the world from that data.
Deep learning requires very powerful machines to work on the data and is very useful in detecting the data patterns from the input data.
Example of Deep learning
Voice to text or text to voice is an example of the deep learning technology. A computer can gather so much insight from the voice or the text that it can inter-convert them into one and the other. Similarly, there are many deep leering systems that can so profoundly copy or mimic the voice of a human that it is so difficult to distinguish between either of them.
What is Data Science?
Data science is not a subset but an overwhelming knowledge that interacts with various other branches of data related systems. It involves the extraction of simple or raw data from various other complex sources and indulging in the processing, analyzing, and visitation of that data. In simpler terms, data science is all about understanding and making sense of the data at hand.
Example of data science
Statistics can be the example of data science in which data is extracted, it is analyzed and processed to produce a simpler output in the form of an answer to the question being asked. There are various examples of data science but all of these revolve around the concept of data extraction, processing, and analyzing it.
If you are interested in becoming a data scientist then data science certification is a must-have for you. Becoming a data scientist will normally open tons of opportunities for you to improve your career in the long run.