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                                                                                                                           References

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                                                                                                                           References

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                              Disadvantages of Machine Learning

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                              Disadvantages of Machine Learning

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                         Advantages of Machine Learning

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                         Advantages of Machine Learning

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                                                    Machine Learning

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                                                    Machine Learning

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                                           Working Of Machine Learning

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                                           Working Of Machine Learning

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                         How does it work? 

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                         How does it work? 

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                                         Timeline of Machine Learning

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                                         Timeline of Machine Learning

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                                History of Machine Language

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                                History of Machine Language

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In 1959, Arthur Samuel, a founder of IBM (International Business Machines), employed the word Machine Learning (ML) for the first time. The name derives from scientists who have observed computers that recognize patterns and who have developed the idea that computers can learn without being programmed for certain tasks.
We started to study artificial intelligence and see how data is learned by computers. ML started to pick up in the 90's as it became its own special field rooted by mathematical analysis and probability theory, isolated from artificial intelligence.
ML developed further into many programming languages, all of which were part of the ML family. C++, Python   are the two most common languages today. Technologies and concepts from Cyclone, Elm, Nemerle, Haskell and many others had inspired the ML languages.

(ML: Get Started With Metaprogramming, 2020)

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* Machine Learning
* History Of Machine Learning
* How Machine Learning works?
* Advantages And Disadvantages of Machine Learning
* References

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•Supervised machine learning algorithms can use designated examples to predict future events and apply what has been learned in the past and new data. The learning algorithm generates a deduced function to provide predictions of the values.

•Unsupervised machine learning algorithms are used where training information is not labelled or counted. Uncontrolled learning explores how structures can extract function from unmarked data in order to explain the hidden structure.

•Somewhere between supervised and unsupervised instruction, semi-supervised machine learning algorithms use both marked and unmarked knowledge for education.

•Reinforcing machine learning algorithms is an educational method that interacts and discovers errors or benefits in the system.

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                                                Different Algorithms

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                                                Different Algorithms

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Machine learning is a technology of artificial intelligence (AI) that provides systems the ability to automatically learn and acquire information without complex programming.Technology education focuses on the creation of computer programs that can access and use data on their own.In order to search for data trends and make informed decisions in future based on our examples .The learning process starts with observations or data, such as instances, direct experience or instructions.The primary objective is to allow computers to learn automatically and change behavior without human involvement or assistance.
(What is Machine Learning? A definition - Expert System, 2017)

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                                                    Machine Learning

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                                                    Machine Learning

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Insights, S., 2020. Machine Learning: What It Is And Why It Matters. [online] Sas.com. Available at: <https://www.sas.com/en_ae/insights/analytics/machine-learning.html> [Accessed 21 April 2020].

SevenTablets, Inc. 2020. What Are The Pros And Cons Of Machine Learning?. [online] Available at: <https://seventablets.com/blog/what-are-the-pros-and-cons-of-machine-learning/> [Accessed 21 April 2020].
Expert System. 2017. What Is Machine Learning? A Definition - Expert System. [online] Available at: <https://expertsystem.com/machine-learning-definition/> [Accessed 20 April 2020].
WhoIsHostingThis.com. 2020. ML: Get Started With Metaprogramming. [online] Available at: <https://www.whoishostingthis.com/resources/ml/> [Accessed 18 April 2020].

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Machine learning is a type of Artificial Intelligence that teaches computers to think in the same way that a human does. Do different tasks like learn and reflect on past experiences.
(Insights, 2020)

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Data Acquisition – machines requires massive data sets to train on and they should be of good quality.

Time consuming – they are able to process large amount of data. The larger the volume of data, the more time it takes to process the data.

High error possibility – training and testing of data inaccurately can result in different errors.

Selection of Algorithm – the type of algorithm used has to be manually picked by a human.

(What Are the Pros and Cons of Machine Learning?, 2020)

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They are able to easily identify patterns and trends.

Improves over time – efficiency and accuracy improves with time.

No human intervention – they can adapt instantaneously without the need for human interaction.

Automation – they are able to automate various decision-making tasks.