What Is Machine Learning?
machine learning is a topic within Artificial intelligence, which is initially unable the software to learn from previous data based on the result provided. in order to have a more efficient solution, machine learning uses a variety of algorithms. In some cases, it can predict the future, if we provided it with appropriate and constantly changing data sources. This is why most businesses have started using Machine Learning nowadays, as it can give a variety of efficient solutions along with market production.
Machine learning applications are probably part of your everyday life without you realizing it. Upon visiting an e-commerce site and viewing a product and reading the reviews, you are likely to be shown other similar products. In order to recommend other similar products that you may want to purchase, the model takes into account your browsing history and other shoppers' browsing and purchasing data (Hurwitz and Kirsch, 2018).
How long has Machine Learning been around?
There is nothing new about AI and machine learning algorithms. Artificial Intelligence has existed since the 1950s. Among the earliest machine learning programs was developed by Arthur Lee Samuels: a program for playing checkers that learned on its own. Arthur Lee Samuels introduced the term "machine learning" in the 1970s. An article published in the IBM Journal of Research and Development in 1959 explained his approach to machine learning (Hurwitz and Kirsch, 2018).
What are the areas of machine learning?
Machine Learning (ML) is a massive field to cover, the following diagram shows the main category and the sub-category for each one ;
(business2community, 2017)
There are 3 main categories:
1. Reinforcement learning (RL):
RL is a prototype, which allows the machine/software to develop based on previous errors and results using the human method. While humans learn from life experience, Machines/Software learn from the feedback they receive from the environment and improve the flows according to the feedback (Medium, 2017).
2. Supervised Learning (SL):
SL is used to predict the outcome X of an input Y and that is after a deep analysis of unseen data. SL is one of the most important methodologies in Machine Learning as it affects other areas such as classification and regression. (Cunningham, Cord and Delany, 2008)
3. Unsupervised Learning (USL):
An unsupervised learning model does not require supervision by the users. Rather, it allows the model to detect patterns and information on its own that were previously unnoticed. Specifically, it works with data that has not been labelled (Unsupervised Machine Learning: Algorithms, Types with Example, 2022)
What are ML's uses and benefits?
Machine Learning is used in many areas, the main ones are:
- E-commerce and business analysis
- Video Game Industry
- Data analysis
- Image recantation
- Crime Investigation
- Wide Range of Applications
- Efficient Handling of Data
- Best for Education and Online Shopping
References:
Guru99. 2022. Unsupervised Machine Learning: Algorithms, Types with Example. [online] Available at: <https://www.guru99.com/unsupervised-machine-learning.html> .
Hurwitz, J. and Kirsch, D., 2018. Machine Learning for dummies a Wiley brand. John Wiley & Sons, Inc., pp.4-5.
Medium. 2017. 6 areas of AI and machine learning to watch closely. [online] Available at: <https://medium.com/@NathanBenaich/6-areas-of-artificial-intelligence-to-watch-closely-673d590aa8aa>.
techvidvan. 2022. Exploring the Advantages and Disadvantages of Machine Learning. [online] Available at: <https://techvidvan.com/tutorials/advantages-and-disadvantages-of-machine-learning/> .
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