Technology, technology & technology, Technology is everywhere now. It has made rapid progression from the last few decades and it continues to bring up several innovations in the world. From the discoveries and new intelligence, we are getting innovative products. Artificial Intelligence, Data Science and Machine Learning have taken new wings and continuously researchers or scientists are exploring the new out of these. Talking about all of the three terms, all are inter-related. People are talking about these things but they are still confused regarding the differentiation between them. In this blog we have tried to resolve this confusion by stating some relevant points.

career in machine learning jobs
a career in machine learning jobs

Artificial Intelligence Vs Machine Learning Vs Data Science

Overview

The process of “making machines intelligent”  so as they can develop their thinking ability to make decisions according to the situations is known as Artificial Intelligence.

The process in which “the pathway of making the machine intelligent” is performed is known as Machine Learning.

The process of “utilizing machine learning algorithms for analyzing data and making predictions” is called Data Science.

Artificial Intelligence Vs Machine Learning Vs Data Science

  1. Definition

Artificial intelligence: The process of making computer thinking just like humans.

Machine Learning: It is a subset of AI in which systems learn automatically from past experiences.

Data science: The process of extracting knowledge from structured and unstructured data.

  1. Skills Required

Artificial intelligence: Algorithms, Probability, Statistics, Python/R/Java, UNIX Tools.

Machine Learning: Computer Fundamentals, Data Modeling & Evaluation, ML algorithms, Software Engineering, Problem Solving.

Data science: Programming skills, Machine Learning, Statistics, Probability, Data Visualization, and Data Wrangling.

  1. Programming language

Artificial intelligence: Python, C++, Java, Lisp, and Prolog

Machine Learning: Python, C++, Java, Lisp and Prolog, Scala, Shell, R, and TypeScript

Data science: Python, R, Scala, SQL, Scala, and Julia

  1. Hardware requirements

Artificial intelligence: CPUs(Intel Scalable Processor), Intel 17 bit Qubit, superconducting chip, Intel FPGAs and Special purpose built-in silicon.

Machine Learning: TPU, GPU- NVidia TitanX Pascal, Processor, Motherboard, and Stormtrooper cabinet

Data science: CPU — 2 Intel Xeon SP Gold 5217’s, 8 core / 16 thread each @ 3.0Ghz.

  1. Tools Used

Artificial Intelligence: TensorFlow, Scikit Learn, Keras, and Open NN

Machine Learning: TensorFlow, Scikit Learn, Weka and KNIME

Data Science: SAAS, Apache Spark, BigML and MATLAB

  1. Types

Artificial Intelligence: Reactive Machine, Theory of mind, Self-awareness and limited memory.

Machine learning: Supervised, Unsupervised and reinforcement learning

Data science: Supervised, Unsupervised and reinforcement learning

  1. Type of data used

Artificial Intelligence: Standardised, Data in terms of embeddings and vector

Machine learning: Structured, Unstructured, Continuous and Discrete

Data Science: Structured and Unstructured

  1. Working Process

Artificial Intelligence: Data->Fast processing->Algorithms->Learn automatic->Give result.

Machine Learning: Data->Select Model->Train->Evaluate->Make predictions.

Data Science: Data->Analyse->Clean->Validate->apply algorithms->Get output.

  1. Career Choices

Artificial Intelligence: Algorithms specialists, Computer Engineers, Computer scientists, Surgical Technicians, and Research Scientists

Machine Learning: Machine Learning Engineer, Data scientist, Cloud Architects, Cyber and Security analyst

Data Science: Data scientist, Application Architect, Business Intelligence Developer, and Data Analyst.

  1. Packages offered

Artificial Intelligence: Artificial intelligence scientists or computer scientist in this category takes around $100,000 and rise to $150,000 annually.

Machine Learning: Machine learning engineer offers a salary of  $114122 approx and it can vary depending on the role.

Data Science: Data scientists are making handsome salaries ranging from  $91,4711 to $130,000 annually.

  1. Market Demand

Artificial Intelligence: The global enterprise AI market size was valued at $4.69 billion in 2018, and is probable to reach $53.06 billion by 2026, recording a CAGR of 35.5%  from 2019 to 2026.

Machine Learning: According to statistics the global market size was $4.99  billion in the year 2018 and will take a value of $35.35 billion by year 2025.– The total funding allocated to machine learning globally during the onset of 2019 was almost $28.4 billion.

Data Science:  Demand is increasing day by day, according to reports by great learning approx. 1.5 lakh job openings will be published in the year 2020 and 71% of jobs post in this area only belongs to data scientists having experience less than 5 years.

  1. Examples

Artificial Intelligence: Google’s AI-powered prediction, spams, plagiarism, Robo-readers Machine Learning: Virtual personal assistants, Video Surveillance, E-mail Spam and   malware detection

Data Science: Oncora medical, Targeted advertisement and Website recommendations

Artificial Intelligence, machine learning, and data science play a vital role in every aspect of technologies these days. We have briefly learned Artificial Intelligence vs Machine Learning vs Data Science. We also learned the features which differentiate these from one another. The race for investigation of AI, data science and machine learning is still exploring to another level and will continue to explore better and innovative.

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