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Machine learning is computers' learning from existing examples to guess well on new examples. It involves the use of statistical techniques and algorithms to enable machines to learn and make predictions or decisions based on data. Machine learning algorithms typically focus on tasks like classification, regression, clustering, and more, where the algorithm learns from data to make predictions or discover patterns. Some algorithms are not machine learning algorithms, for instance, the KMP algorithm is a classic computer science algorithm designed for efficient string matching and does not involve learning from data or making predictions so that it is not a machine learning algorithm.

Machine learning is defined by:
         i) It is about mapping the inputs to targets, which is done by observing many examples of the inputs and targets.
         ii) It is a field of study and application that focuses on the process of mapping inputs to targets or outputs by observing and analyzing numerous examples of input-output pairs. It involves training algorithms or models to understand and generalize from these examples, enabling them to make predictions or decisions on new, unseen data based on the learned patterns.

Artificial intelligence is a discipline; machine learning is a specific way of solving AI problems. The programs for Machine Learning (ML) become more data-driven, in terms of making decisions or predictions. Machine learning is a is a subset of Artificial Intelligence (AI) as well as a subfield of computer science which is evolved from the study of pattern recognition and computational learning theory in Artificial Intelligence (AI). Machine learning transforms the way we understand and interact with the world around us. Nowdays, machine learning is rapidly changing our world and is rapidly becoming a fixture of our daily lives. Through subtle but progressive improvements where we interact with computers and the world around us, machine learning is progressively making our lives better. In 1959, Arthur Samuel, an American pioneer in the computer gaming field, machine learning, and artificial intelligence has defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed." Overall, machine Learning is a collection of the analysis and construction of algorithms and techniques used to create computational systems that learn from data in order to make predictions and inferences on data. The trick with machine learning is to build a model that generalizes to new cases besides memorizing past cases.

As a part of AI, machine learning mainly comprises three types:
         i) Supervised machine learning,
         ii) Unsupervised machine learning,
         iii) Reinforcement learning.

There are many subfields of machine learning:
         i) Scientific applications for predictive analytics [1]. In this application, machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data driven predictions or decisions, rather than following strictly static program instructions.
         ii) Be able to contribute to an interesting array of different problems,
         iii) Clustering analysis, which can help us to find hidden structures in data even if, in many cases, our training data does not come with the right answers to learn from.
         iv) Reinforcement learning, in which the goal is to develop a system (agent) that improves its performance based on interactions with the environment.
         v) Computer science that involves using statistical methods to create programs that either improve performance over time, or detect patterns in massive amounts of data that humans would be unlikely to find.

Some machine Learning examples [1-4] are:
         i) Bag-of-words model. This converts the phrases or sentences and counts the number of times a similar word appears.
         ii) Recommendation System:
            ii.a) YouTube and Spotify brings videos and sounds for each of its users based on a recommendation system that believes that the individual user will be interested in.
            ii.b) Amazon and other such e-retailers suggest products that the customers will be interested in and likely to purchase by looking at their purchase history for customers and a large inventory of products.
         iii) Spam detection:
            iii.a) Email service providers, e.g. Gmail and Outlook, use a machine learning model that can automatically detect and move the unsolicited messages to the spam folder.
        iv) Prospect customer identification:
            iv.a) Banks, insurance companies, and financial organizations use machine learning models to trigger alerts so that organizations
intervene at the right time to start engaging with the right offers for the customer and persuade them to convert early.

Table 4514. Three components of learning algorithms used in machine learning.

Machine learning models, like many technologies, will likely never be perfect. They are designed and trained to approximate or generalize from the data they are given, which inherently includes limitations and imperfections. Models can be very effective for a wide range of tasks, but they may still make errors, struggle with complex nuances, or fail in unpredictable ways, especially when confronted with scenarios that deviate from their training data. Their performance can continually improve, but achieving absolute perfection is unlikely due to these inherent constraints.         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         
         

[1] Daniel Kurian, Fereshteh Sattari, Lianne Lefsrud, and Yongsheng Ma, Using machine learning and keyword analysis to analyze incidents and reduce risk in oil sands operations, Safety Science, 130(2020), 104873.
[2] Khakzad, N., Khan, F., Amyotte, P., 2013. Dynamic safety analysis of process systems by mapping bow-tie into Bayesian network. Process Saf. Environ. Prot. 91 (1–2), 46–53.  
[3] Xu, Q., Xu, K., Li, L., Yao, X., 2018. Safety assessment of petrochemical enterprise using the cloud model, PHA–LOPA and the bow-tie model. R. Soc. Open Sci. 5 (7), 180212.  
[4] Zhao, J., Cui, L., Zhao, L., Qiu, T., Chen, B., 2009. Learning HAZOP expert system by case-based reasoning and ontology. Comput. Chem. Eng. 33 (1), 371–378.  
[5] Dan Ofer, Machine Learning for Protein Function, thesis, 2018.