Main Project :
Machine learning And AI
Machine learning is a branch of Artificial Intelligence, which can be defined as the ability of machines to emulate intelligent human behavior with the use of algorithms, gradually increasing the accuracy. Algorithms are trained to make classifications or predictions with the use of statistical methods( IBM Cloud Education, 2020).
Machine learning is transforming technology and has a number of benefits to several major sectors, these include;
Healthcare:
Machine learning can be used to identify illnesses, for instance cancer cells or heart diseases. It can also used for specified treatments based on a patient's genetic makeup. With the help of Automation, apps can analyze a patient's condition.
Education:
In Education, machine learning can be used to detect and uncover cheating and plagiarism among students. It can help students achieve greater results in school by creating tailor made lessons for each student.
Transportation:
Machine learning can be used to predict traffic patterns and optimize routes, which save time and money for businesses.
Marketing:
Real time marketing predictions such as customer preferences and predicting what products customers are likely to purchase. These predictions are made precise by machine learning.
Fraud Detection:
There are a great number of Digital transactions taking place in the world today and it is monitored by machines in order to prevent and identify fraudulent activities in financial transactions.
Data Mining:
Machine Deep learning can extract valuable information from large data sets. This can be used improving business operations by predicting outcomes.
(Team Adservio, 2022)
Machine learning can be useful in modern day technology however, there are some drawbacks in this field for instance;
Data acquisition and inconsistency:
A machine can misinterpret data when the model does not get data in a consistent and timely format which can lean to a tear down in its decision and control. Hence a large number of data with concurrent updates is needed.
Prone to error:
ML models deal with a fair amount of big data and this can quite a number of errors when they get tested. It then becomes almost impossible to sort out the whole content and programs due to the sheer size of the data.
Time taking and amount of resources needed:
Developing even a single model of machine learning with require a great deal of resources and the time needed for machines to learn would also take years to be achieved.
Excessive use may harm mankind:
Machine learning is evolving exponentially and it can make human obsolete due to the fact that machines would eventually replace humans in the workspace and this can lead to unemployment for many.
(Aarya, 2022)
The value, importance and the potential of what AI could do for mankind and the world far outweighs the few drawbacks.
ML deals with displaying large amounts of data to a machine to enable it learn, to predict or to classify data. There are majorly three ways machines learn, which are supervised, unsupervised and reinforcement learning.
Supervised learning
Supervised learning works by providing previously known input and data into the ML algorithm. In order to create an output as close as achievable to the desired result, the Machine would process each step one after the other. In supervised learning the machine is being provided with an algorithm to help it learn. Supervised learning is useful for a number business purposes for instance in banks to determine fraud, in real estate to predict prices, classifying the nature of an email.
Unsupervised learning
Unsupervised Learning deals with creating a predictive model that classifies data based on properties, this model is known as clustering. This type of learning can be used to create data for future supervised learning by recognizing patterns or features. Use cases include grouping customers by amount of purchases they make, classifying customers based on preferences.
Reinforcement Learning
This type of learning deals with rewarding positive outcomes and punishing the negative ones. This is quite like how humans learn. In this type of learning the algorithm produces a number of output and it is trained to pick the right one based on certain situations. Cases where this can be used are in teaching a car to park itself, solving traffic jams by flexibly changing traffic lights.
( Rimol, 2020)
Machine learning has become a huge part of the world today. There are a few others ways it impacts our lives on a daily basis. These include:
Social media: Most social media platforms develop algorithms that make suggestions based on your history or preferences. The algorithm learns from the activities of users, for instance comments made or time spent viewing certain content.
Virtual assistant: Machine learning is a key part of the devices used today as certain smart devices posses voice activating virtual personal assistants that can perform tasks when asked for instance Apple's Siri, Amazon's Alexa. information is gathered and filtered in order to perform specific tasks better.
Popular recommendations: Certain e-commerce websites use machine learning applications. It enables sites track your recent purchases, searches and uses that information to makes suggestions for purchases in the future,
Image recognition: This kind of technology is used in a variety of areas from uploading a photo on social media or recognizing a user tagged in a photo to serving as a form protection from possible theft or threats by using facial recognition in mobile devices.
(Coursera(updated sept 14), 2022)
References
10 companies using machine learning in cool ways (2021) WordStream. Available at: https://www.wordstream.com/blog/ws/2017/07/28/machine-learning-applications (Accessed: October 27, 2022).
Aarya, S. (2022) Advantages and disadvantages of machine learning, Corporate Review. Available at: https://thecorporatereview.com/advantages-and-disadvantages-of-machine-learning/ (Accessed: October 22, 2022).
Advantages and disadvantages of Machine Learning: Ivy pro school (2021) Ivy Professional School | Official Blog. Available at: http://ivyproschool.com/blog/advantages-and-disadvantages-of-machine-learning-in-2020/ (Accessed: October 27, 2022).
Coursera (2022) 3 types of Machine Learning You should know, Coursera. Available at: https://www.coursera.org/articles/types-of-machine-learning (Accessed: October 23, 2022).
Kapoor, V. (2020) Machine learning and its applications, LinkedIn. Available at: https://www.linkedin.com/pulse/machine-learning-its-applications-vidhi-kapoor/ (Accessed: October 27, 2022).
Leetaru, K. (2019) Why machine learning needs semantics not just statistics, Forbes. Forbes Magazine. Available at: https://www.forbes.com/sites/kalevleetaru/2019/01/15/why-machine-learning-needs-semantics-not-just-statistics/?sh=19749b4377b5 (Accessed: October 27, 2022).
Machine learning (in finance) (2022) Corporate Finance Institute. Available at: https://corporatefinanceinstitute.com/resources/knowledge/other/machine-learning-in-finance/ (Accessed: October 27, 2022).
Rimol , M. (2020) Understand 3 key types of machine learning, Gartner. Available at: https://www.gartner.com/smarterwithgartner/understand-3-key-types-of-machine-learning (Accessed: October 23, 2022).
Soffar, H. (2019) Machine learning overview, definition, tools, applications, Advantages & Disadvantages, Science online. Available at: https://www.online-sciences.com/robotics/machine-learning-overview-definition-tools-applications-cons-pros/attachment/machine-learning-2/ (Accessed: October 27, 2022).
IBM Cloud Education (2020) What is machine learning?, IBM. Available at: https://www.ibm.com/uk-en/cloud/learn/machine-learning (Accessed: October 20, 2022).
Team, Adservio. (2022) Machine learning: Types: Benefits, Adservio. Available at: https://www.adservio.fr/post/machine-learning-types-benefits#:~:text=It%20is%20a%20type%20of,efficiency%2C%20and%20decision%2Dmaking. (Accessed: October 21, 2022).
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