Artificial Intelligence vs. Machine Learning vs. Data Science

If you have an eye for an IT certification such as data analyst certificate or even an online data science certificate, some common catchphrases you are likely to come across include;

  • Machine Learning
  • Artificial Intelligence and
  • Data Science

Obviously, you may pick curiosity about what these buzzwords stand for, and you may even be wondering if there is a linkage between any of them. Well, in simple terms, I'll tell you that these words make up a significant part of the IT world. Like you may rightly guess, they are interrelated at some points in your training for an online data science certification or data analyst certificate. So without much ado, let's take a closer look at these "big" words that you may come across often in your career path as a data scientist or data analyst.

What is Artificial Intelligence (AI)?

Without any attempt to take you through long stories, artificial intelligence simply refers to the act of simulating machines to function like the human brain. In artificial intelligence as a discipline, machines are made to analyze information and draw possible conclusions as a human would do. Some typical human functions which machines can be equipped to perform in AI include learning, self-correction, and logical reasoning.

Generally, artificial intelligence is grouped majorly into two distinct parts thus;

General AI

In the aspect of general AI, machines are designed with some level of intelligence to analyze situations in a vast area of activities that involves thinking and reasoning just like humans do.

Narrow AI

In Narrow AI, machines’ artificial intelligence is used only for a particular task. In real-time experiences, general AI can be likened to an algorithm that is designed to play different kinds of board games. On the flip side, when it comes to narrow AI, this may be likened to limiting the range of a machine’s intelligence to playing a particular game like monopoly or chess. Currently, advances in AI are only limited to narrow since this is the only area where IT researchers and developers have been able to record some success. On the contrary, when it comes to general AI, researchers are still making an effort to achieve this, and it is likely going to take a long time for this to come true (only if it will ever be possible)

What Is Machine Learning (ML)?

Once a computer system can improve upon itself by learning from the environment with undergoing explicit programming, such a computer system is said to undergo machine learning.

The major focus of machine learning is to enable algorithms that will enable a computer system to learn from available data.

ML also involves helping machines to gather insight from such data as well as make inferences from data that hasn't been analyzed previously using the new information that is being provided.

Machine learning operates at three basic models, which include;

  • Supervised learning
  • Unsupervised learning and
  • Reinforcement learning

Supervised Machine Learning

In the case of supervised machine learning, data that are labeled are the tools used to enable machines to recognize certain characteristics that will help the machine to analyze similar data in the future. For instance, when classifying the pictures of boys and girls, you have to first feed in a few labeled pictures of different boys and girls. With this, the machines can then identify and classify other similar pictures for you in the future.

Unsupervised Machine Learning

In the case of unsupervised learning, data that are unlabeled are just fed into the machine, and the machine will identify some common features among the pictures and them classify them accordingly.

Reinforcement Machine Learning

In Reinforcement machine learning, there is an interaction between the environment and algorithms to produce actions that then analyze errors as well as rewards. For instance, when trying to understand scrabble, ML algorithm will not only analyze a single word but will study every possible word as well as the game in general.

What is Data Science?

Even without being told, a random guess will help you to arrive at the assumption that data science is simply about data. Well, besides this, it is worthy of note to mention here that data science goes beyond the manipulation of data to making business decisions. In case you may be wondering how here is how data science helps in decision making in businesses. Every business, at some points, collects a large amount of data, which will enable them to gain more insight into how their business is thriving. Well, to properly analyze and make intelligent inferences from data collected, a data scientist with at least a data science certification is required. With this, you can easily conclude and bring out patterns in data that you may literally never knew existed. More so, various companies build recommendation engines using data science. With this, they can easily predict the behavior of users as well as other characteristics. Well, to ascertain such characteristics about users, enough data has to be generated for the application of different algorithms, which will help you to arrive at some accurate results about the user's characteristics. However, in some cases, the data to be analyzed as well as the criteria to be considered when analyzing such data may seem more of artificial intelligence. But the truth remains that so far data or rather, a huge data set is involved, and some machine learning algorithms are applied to such data, such data analysis is a part of data science. This, in part, stands as the major reason why AI is a core aspect of data science when undergoing some online data science certification courses as well as data analyst certification.

What Is The Different Between AI, ML, and Data Science?

  • Artificial Intelligence is a compound term with lots of applications in robotics as well as in text analysis. Well, AI is still being seen as a technology that is still undergoing evolution.
  • Machine Learning focuses more on a narrow range of activities, and it is usually seen as a subset of AI. As a matter of fact, ML is the only form of AI that is seen to have some real-world problem-solving capabilities.
  • Data Science can't be out-rightly called a subset of ML, but in most cases, data science utilizes ML in the analysis of data collected to make some concrete predictions about what the future will look like.

However, data science doesn't work only with ML; it combines another discipline such as cloud computing and big data analytics in the prediction of the future from data analyzed. With this, we can comfortably say that data science is the only practical approach of ML that has some real-world problem-solving potentials.

Wrap Up

AI, ML, and data science are all core disciplines in the IT world, which are involved in the manipulation of data at various levels to solve some real-world problems. While AI seems to be an all-encompassing discipline that cuts across both data science and ML, there is still much to uncover about this amazing technology. More, so, AI is hoped to go a long way in helping humans solve various analytical problems with the use of machines and computers.

About The Author

Data Science Instructor

David Odgers

Results-oriented Data Scientist with 7+ years’ experience in Machine Learning, Artificial Intelligence, Cloud Computing, and Big Data Platforms; 11+ years’ experience as an Analytics Professional; and 4+ years’ Consulting experience in the aforementioned areas. Proven track record of delivering innovative Cloud-Based Machine Learning solutions for both Fortune 500 and start-up companies.