Artificial Intelligence vs Machine Learning vs Data Science

12/10/2020

Artificial Intelligence vs Machine Learning vs Data Science | by Mantha Anirudh | Dec, 2020 | Medium

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Artificial Intelligence vs Machine Learning vs Data Science Mantha Anirudh 21 hours ago ¡ 3 min read

Artificial intelligence Modern technologies like Artificial Intelligence, Machine Learning, Data Science and Big Data have become the buzzwords everyone speaks, but no one fully understands.

They look too complicated for a commoner. All of these buzzwords are similar to those of a business executive or a student from a non-technical background. People are often confused with terms like AI, ML and data science. In this blog, we will explain these techniques in simple terms so that you can easily understand the difference between them and how they are used in business.

Read more: AI & ML Use Cases Across Eight Industries

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12/10/2020

Artificial Intelligence vs Machine Learning vs Data Science | by Mantha Anirudh | Dec, 2020 | Medium

What is Artificial Intelligence (AI)?

Artificial intelligence refers to the simulation of the human brain function by machines.

This is achieved by creating an artificial neural network that can show human intelligence. The essential human functions that an AI machine performs are logical reasoning, learning and self-correction. Artificial intelligence is a vast field with many applications, but it is also a very complex technology to work with. Machines are not inherently smart, and we need a lot of computing power and data to make them, to empower them to emulate human thinking.

AI is divided into two parts, General Artificial Intelligence and Narrow Artificial Intelligence. General AI refers to the intelligent transformation of machines into a broader range of thought and reasoning activities. Narrow AI, on the other hand, is the use of artificial intelligence for a particular task. For example, simple AI means an algorithm capable of playing all types of board games, while narrow AI limits the range of machine capabilities to a specific game, such as chess or scrabble. Currently, narrow AI is only within the purview of developers and researchers. General AI is the dream of researchers and public awareness that it will take a long time for the human race to achieve (if ever possible).

Know more: Top 50 AI Companies in US, India & Europe What is machine learning?

Machine learning (ML) is the ability to learn from the computer system environment and improve oneself from experience without the need for explicit programming.

Machine learning focuses on learning algorithms from the data provided, gathering insights and making predictions on previously analyzed data using the collected information. Machine learning can be done using multiple approaches. Three basic models of machine learning are supervised, supervised and reinforcement practice.

In the case of supervised practice, labelled data is used to identify features and assist machines for future data. For example, if you want to categorize images of cats and dogs, you can play with some labelled image data, and the machine will classify all other images for you. On the other hand, in the unsupervised practice, we put unlabeled data and let the mechanism understand the characteristics and classify it.

Reinforcement machine learning algorithms interact with the environment by generating actions and then analyzing errors or rewards. For example, the ML https://anirudh-m.medium.com/artificial-intelligence-vs-machine-learning-vs-data-science-49927076070b

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12/10/2020

Artificial Intelligence vs Machine Learning vs Data Science | by Mantha Anirudh | Dec, 2020 | Medium

algorithm does not analyze individual movements to understand a chess game but studies the game as a whole.

What is Data Science?

Data science means extracting relevant insights from data sets. It uses a variety of techniques from a variety of fields, including mathematics, machine learning, computer programming, statistical modelling, data engineering and visualization, model recognition and learning, uncertainty modelling, data warehousing, and cloud computing. Data science does not necessarily involve big data, but the fact that data can be scaled makes big data an important aspect of data science.

Data Science AI, ML and the most widely used data-driven technology in it. Data science practitioners generally specialize in mathematics, statistics, and programming (although none of these three are required). Data scientists solve complex data problems to bring data, insights, and correlation patterns into a business.

Read more about the key : The Difference between Artificial Intelligence & Machine Learning

AI

Artificial Intelligence

Machine Learning

Data Science

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