You must have heard about these terms Data Science, Machine Learning, Artificial Intelligence, Neural Networks, Deep Learning, and many more. But what do these terminologies really mean? And for what reason would it be a good idea for you to think about any of these?
Raise your hand if you’ve been trapped in the dilemma of distinguishing between Data Science, Machine Learning, Artificial Intelligence, and Deep Learning.
Bring down your hand, pal, we don’t want to see it (smirk).
Apparently, the three buzzwords are normally used conversely and do not really allude to similar things.
So, in this article, I’m trying to answer all these questions to the best of my knowledge. This is the knowledge I’ve earned in the last couple of years of my Machine Learning and Artificial Intelligence journey.
As it gets obvious from the above illustration of three coaxial circles where Deep Learning is a subdivision of Machine Learning, which is also a subdivision of Artificial Intelligence.
Thought-provoking much?
Therefore, Artificial Intelligence is the widely inclusive idea that first flared up, followed by Machine Learning that flourished later, and finally, Deep Learning that is promising to raise the advances of Artificial Intelligence to another level.
How about we burrow further with the goal that you can comprehend which is better for your particular use case: Data Science, Artificial Intelligence, Machine Learning, and Deep Learning.
Let’s begin this.
What is Data Science?
Data science is all about data, and I’m pretty confident you definitely knew about this. But did you realize that we utilize data science to settle on business decisions? I’m fairly sure you knew that too.
So, what more? Well, do you have any knowledge about the procedure of making business decisions through Data Science? Let’s take a roll on this one.
We all very well know that each and every IT company out there is gathering vast amounts of information. The more data you possess, the more business observations you can produce. By making use of data science, you can reveal patterns in information that you didn’t know existed.
For example, you can find that an individual who visited London for vacation purposes is destined to overdo it on an extravagance trip to Paris in the following two weeks. That’s an example that I just quoted, probably won’t be real in reality. But if you’re an organization offering extravagance trips to exotic locations, you might be keen on getting this person’s contact number.
Data science is a multifaceted terminology for an entire set of techniques and tools of data algorithm and induction development to tackle complex analytical problems.
It uses scientific procedures, techniques, and calculations to get it going.
Ab initio, the ultimate objective was to determine concealed patterns in raw data to assist a business with increasing and elevating their profits.
The jargon Data Science become a fuzz word when Harvard Business alluded to it as “The Sexiest Job of the 21st Century”.
Following are the six different phases of Data Science:
Data science is being widely utilized in the company’s scenarios. Organizations are making use of data science to generate recommendation engines, anticipating client’s conduct, and many more. All of this is only conceivable when you possess a sufficient amount of information with a goal that several algorithms could be applied to that information to provide you with more reliable outcomes.
Likewise, it is something many refer to as prescriptive analytics in Data Science, which does kind of the similar forecasts that we discussed in the rich traveller’s example above.
But as an additional advantage, prescriptive analytics will also let you know about the type of luxury trips to Paris a person might get interested in.
For example, one guy might want to travel in the first-class section but would approve of a three-star stay, while someone else could be ready to travel in the economy class but would surely need the most sumptuous accommodation and social experience.
Although, both the individuals are wealthy customers along with different prerequisites. So, you can make use of prescriptive analytics for such situations.
Data Science works amazingly in four interrelated areas:
You might be pondering, hey, that really sounds like Artificial Intelligence!
Voila! You’re not so much off-base, actually. Because running these AI calculations on massive databases is again a chunk of data science.
Artificial Intelligence is utilized in data science to make forecasts and furthermore to explore patterns in the information. This again seems like we’re adding knowledge to our framework.
That ought to be artificial intelligence. Isn’t that so? Let’s see.
What is Artificial Intelligence?
As the name itself proposes, Artificial Intelligence can be deciphered to mean using human knowledge or intelligence into machines.
Artificial intelligence is the capability that can be conferred to computers which let the machines to comprehend the information, gain from the information, and form decisions based on patterns concealed in the data, or derivations that could somehow be extremely tough for humans to make physically.
Artificial Intelligence likewise empowers the machines to alter their “knowledge” based on new sources of information that were not part of the data utilized for instructing these machines.
Artificial Intelligence intends to actually reproduce a human mind, the manner in which a human cerebrum thinks, functions and works.
In reality, we can’t build up an appropriate AI till now, however, we are very near to establishing it up soon.
Sophia, a legit example of Artificial Intelligence, is an exceptional AI model till the date. The reason for not establishing a proper AI model till now is, we are still not certain and have foggy ideas about the functioning of a human brain, like why do we hallucinate? etc.
While in Hollywood films like, Transformers, Artificial Intelligence is depicted as human-like robots that are taking control over the globe. Although, the current development of AI technology is neither that startling nor that smart. Rather, Artificial Intelligence has been profited by various industries, and there are contemporary examples like healthcare, fashion, retail, education, and the sky is the limit from there.
But there’s one thing you have to ensure, that you have ample data for Artificial Intelligence to gain from. If you possess a very little database that you’re utilizing to prepare your AI model, the precision of the forecast or decision could be moderate.
“So more the information, the better is the establishment of the model, and the more precise will be the results“. Contingent upon the size of your data, you can pick different algorithms for your AI model. This is the point where Deep Learning and Machine Learning begin to appear.
Training computers to have a thought process just like humans is accomplished partly using ‘Neural networks’.
Neural networks are a series of calculations modelled after the human mind. Similarly, as the cerebrum can determine patterns and assist us with assorting and classifying the data, neural networks function the same for computers. The human mind is continually trying to comprehend the data it is preparing, and to do this, it marks and allocates items to classifications.
When we experience something new, we attempt to contrast it with a known item to assist us with comprehension and understand it. Neural systems work the same for computers.
In the initial times of Artificial Intelligence, neural systems were extremely popular. There were numerous groups of people all over the world working on bettering their neural systems.
Following are the advantages of Neural Networks:
However, from the late 1980s to the 2010s, Machine Learning came in the scenario. Every big IT company was putting its money heavily in machine learning. Organizations including Google, IBM, Amazon, Facebook, etc. were virtually hiring Artificial Intelligence and Machine Learning Ph.D. individuals directly from their universities.
There’s definitely been an advancement of Artificial Intelligence over the last couple of years, and it’s getting better as time passes by.
What is Machine Learning?
“Hey Siri, could you be able to explain about Machine Learning, please?”
I am pretty much sure that you might have bought something from Amazon. So, while searching for the products, it suggests similar items that you might be looking for as well. Likewise, you might have also noticed that the fusion of items is also being recommended. All in all, have you ever pondered over how does this suggestion occurs?
This is Machine Learning, buddy!
You may have gotten a call from the bank mentioning to get a loan. What do you think, do they make calls to everybody out there? No, they call only chosen clients who are using similar sites or replicas, interested in buying their item. This objective marketing is applied through grouping.
As the name itself proposes,
Machine Learning can be freely deciphered to mean enabling computer systems with the capability to “learn”.
Machine Learning is utilized in scenarios where we want the machine to gain from the gigantic amounts of information, we provide it with, and afterwards, apply that knowledge on new parts of data that flows into the computer system. But how does a machine learn, you may inquire.
There are various modes of making Machine Learning. Several techniques of machine learning are directed towards learning, non-administered learning, semi-administered learning, and strengthened machine learning. In some of these methodologies, a customer instructs the machine about the traits, independent factors (input), or dependent factors(output).
Hence, the machine gets trained about the connection between the independent and dependent factors present in the information that is being given to the machine.
This data which is being given is known as the Training Set. Furthermore, when the learning stage or training period is over, the machine, or the Machine Learning model, is demonstrated on a piece of information that the model has not experienced previously.
This new database is known as the Test Database.
There are various manners by which you can part your current dataset between the training and the test database. When the model is grown enough to give authentic and extremely reliable results, the model will be sent to a production setup where it will be utilized against completely new datasets for issues like forecasts or characterization.
Presently, Machine Learning is dealing with the following issues:
Machine Learning is a subdivision of Artificial Intelligence that particularly concentrates on making forecasts based on customer experiences. It empowers the computer system to settle on a data-driven decision, instead of an explicit program for performing a particular task. The algorithms are designed in a specific way and that way is learned and improved over time that helps the user make a better decision.
Machine Learning is a subdivision of Artificial Intelligence that exclusively focuses on making predictions based on buyer experiences. It enables the computer to make a data-driven decision rather than explicitly program for carrying out a specific task. The algorithms are structured in a specific manner that learns and developed after some time and assists the clients with making a superior decision.
Following are the types of Machine Learning (ML):
For example, a company called ‘Crisis Text Line’ makes use of the Machine Learning to determine which word, when composed in a text message, is destined to forecast suicide. To disconnect words, it utilizes a machine learning technique called Entity Extraction.
Then it utilizes sentiment analysis and natural language processing to make sense of the word “ibuprofen” is multiple times bound to anticipate suicide than the actual word “suicide,” and that the crying face emoticon is 11 times bound to forecast that the person is in an emergency.
With this information on Machine Learning, let’s dig deeper into ‘Deep Learning’ now.
What is Deep Learning?
You can believe Deep Learning models as a rocket engine and its fuel is the enormous amount of information that we provide to these algorithms.
The concept of Deep Learning isn’t new. But currently, its popularity has elevated, and deep learning is becoming an eye-candy.
This field is an exceptional sort of Machine Learning which is encouraged by the functionality of our synapses known as artificial neural networks.
A neural system is a pile of task-specific algorithms that uses profound neural networks that are particularly motivated by the formation and functioning of the human mind.
Deep learning is inspired by hypothetical arguments from circuit theory, current data, instinct, and experimental outcomes of neuroscience. Deep Learning algorithms can be characterized by different kinds and recognized by patterns to give the ideal results when it gets input.
Deep Learning is an advanced version of Machine Learning. However, Machine Learning works amazingly for most apps, there are circumstances where Machine Learning leaves a ton to be desired. That is where Deep Learning steps in the scene.
It is usually considered that if your preparation dataset is moderately small, you choose to stick with Machine Learning. But if you have a tremendous amount of information on which you can prepare a model, and if the data has the high number of characteristics, and if precision is highly potent, then you choose the Deep Learning route.
Likewise, it is also significant to take note of that Deep Learning needs relatively strong devices to run on. It generally requires more effort and time to prepare your models and is usually tougher to execute, when compared to Machine Learning. Yet these are some of the agreements that you have to stay with when the issue you’re trying to unravel is significantly more intricate.
For example, suppose we have an electric lamp and we train a Machine Learning model that whenever anybody says “dim” the spotlight should be on. Now the model will examine various expressions said by people and it will look for the word “dim”.
As the word is being uttered, the spotlight will be on, but imagine a scenario in which somebody said: “I am unable to see anything the light is extremely low”. Under such a case, the user wants the electric lamp to be on, however, the sentence doesn’t include the word “dim” so the lamp won’t be on.
This is where Deep Learning and Machine Learning concepts are different. If it was a deep learning model it would on the lamp.
Now let’s carefully evaluate the difference between Machine Learning and Deep Learning:
Machine Learning VS Deep Learning
| Basis of Difference (BOD) | Machine Learning (ML) | Deep Learning (DL) |
| Feature Engineering | Meticulously comprehend the characteristics of how ML exhibits the data. | Required to comprehend the most superior functionality that exhibits the data. |
| Data Dependencies | Better performance on a small or medium dataset. | Great performance on big datasets. |
| Hardware Dependencies | Works on a low-end machine. | Functions on matrix multiplication. |
| Interpretability | Some algorithms are simple to decipher like, Decision Tree and Logistics. Whereas, some algorithms are complex or impossible to interpret like XG Boost and SVM. | Difficult or even impossible to interpret. |
| Execution Time | It requires few minutes till hours. | It requires at least 2-3 weeks. |
Wrapping Up
Artificial Intelligence (AI) is a broad term that delivers a psychological capacity to a machine while Deep Learning (DL) is an advanced innovation in the field of artificial intelligence. As it empowers many applications of machine learning by the complete expansion in the field of Artificial Intelligence.
Artificial Intelligence (AI) has a splendid future with deep learning assistance. If there is an ample amount of information to prepare the model, then deep learning conveys impressive outcomes, for content interpretation and image recognition.
Do you feel you have a clarity of the contrasts between these different terminologies? I tried to quote a few relevant examples of different applications of the terms too. I hope it makes a difference!
If you are planning to carry on with technologies further and try your hands-on Data Science or Artificial Intelligence knowledge, then you must enroll for one of the pro courses at Vinsys for a greater experience and exposure, thanks to their professional training skills!
As a rear end, careers in each one of these fields are regarded as the best. Thus, you should take up the Data Science and Artificial Intelligence Courses for the betterment of your future 🙂
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Data Science:
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Frequently Asked Questions (FAQs)
When it is ideal to use Machine Learning?
- It is ideal to use Machine Learning when:
- The training time s short.
- The number of algorithms is large.
- The training dataset is small.
What do you mean by Algorithm?
- An algorithm refers to the set of rules to be followed when tackling issues. Algorithms often use data or information as inputs and perform calculations to get an answer.
What is the mechanism of Flipkart suggesting products to us?
- When a customer browses the site, Flipkart’s recommendation framework labels and records the conduct and the movements of the user. Moreover, whenever the client taps on an item, the framework records it.
- Hence, the products are recommended to us through Data Science and Machine Learning.
When it is ideal to use Deep Learning?
- It is ideal to use Deep Learning when:
- The training time is long.
- The number of algorithms is little.
- The training dataset is large.
How does Data Science be related to Artificial Intelligence (AI), Deep Learning (DL), and Machine Learning (ML)?
- Data Science is multidisciplinary that interprets and manipulates the data.
- Moreover, it enables us to discover significance and authentic data from huge databases. This makes it feasible for us to use the information for forming vital decisions in business, technology, science, politics, and many more.
What are the future prospects of Deep Learning?
- In the near future, we can certainly see an upcoming trend where the knowledge about Deep Learning will be the expertise needed by every Data Science professional.
What is the difference between supervised and unsupervised learning?
- In supervised learning, the input data is labeled whereas in unsupervised learning the input data is not labeled.
- Moreover, Supervised learning uses training datasets for prediction, while unsupervised learning uses the input data set for analysis.
