The Evolution of Machine Learning

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The concept entailed in machine learning is aimed at explaining the meaningful patterns and automated detection in data. Over the years, the ideas applied in the machine have been used as a standard tool in handling diverse tasks in the business environment, which learning require extraction of data and information from large sets of data. There are various instances where technology has been incorporated in machine learning to guarantee effectiveness and efficiency in improving the Internet and digital applications. For instance, some machine learning search engines have been developed with the use of technology, which has helped in improving issues related to research and how scholars carry out their studies in various fields across the globe. The other machine-based technologies include the introduction of anti-spam software that aids in filtering email messages as well as software that helps in securing and detecting frauds in an industry. Cars have also been improved with technology that helps in preventing accidents since the machine learning equipment have an inbuilt algorithm (Quinlan 2014, p. 6). The digital cameras that have been introduced in the current society are developed to detect the intelligent personal assistance faces and applications, especially on smart-phones, and they can also be used to recognize voice commands. There are diverse scientific applications that have also incorporated machine learning, and they include astronomy, medicine, and bioinformatics.


The new computing software and technologies have assisted in improving the machine learning technologies from what has been in application over the years. The theory that led to the development of machine learning explains that computers can be used to perform diverse tasks without having programs being installed (Alpaydin 2014, p. 2). The theory also illustrates that researchers who are interested in carrying out artificial intelligence also have the desire to learn about data retrieval and processing with the use of machine learning in undertaking their research and analysis. The concept of machine learning is essential in research studies and other processes undertaken by company managers in industries across the globe, as it helps in improving the iterative procedure and making sure that the new models in the society have been exposed to recent data and information, and the models can also adapt independently. The computations that have been done in previous years are helpful in determining repeatable and reliable results (Alpaydin 2014, p. 2).

Machine learning has been explained to be different from the traditional computers that have been used over the years. The tasks being carried out in machine learning requires execution, which can be difficult to achieve based on the complexity of various patterns that have been introduced by professionals. A human programmer is expected to be equipped with necessary skills and knowledge that will allow them to operate equipment that has the machine learning application. Education obtained from institutions of learning helps programmers develop software, and most of the skills are acquired through the experiences of life. However, the tools used in machine learning have been concerned with diverse endowing programs that have been provided with the ability to adapt and learn (Abadi et al. 2016, p. 265).

Machine learning has been found to appear in various forms, and some applications and data have been identified by scholars to assist in improving the processes involved in technology. Machine learning aims to guarantee reduced problems to create a narrow prototype in the working environment. Therefore, the science applied in machine learning is aimed at provided solutions to various issues and also aiding in decision-making procedures towards attaining the best results of a procedure (Ioffe and Szegedy 2015, p. 448).


Web page ranking is an application that has been accepted by scholars, and assists in ensuring that the search engine being used can receive a query being submitted by a researcher. Once the webpages have been identified and found, an order is returned, which has the relevance of what is expected from the search. The query asked can also be answered with links that assist the user gain more insight and information about the topic, and the process has been effective in ensuring that the automated process of developing a reliable search engine has been attained (Meng et al. 2016, p. 1235).

Collaborative filtering is another application of machine learning that has assisted in increasing the Internet bookstore availability across the globe. The application also faces the same challenges as web page ranking, which include obtaining a sorted list of various products and books that are being sold by the company. Some of the Internet organizations and websites that have incorporated collaborated filtering include Netflix, which is a video rental site as well as Amazon that has been widely adopted by scholars and scientists across the globe. The difference existing between the web page ranking and collaborative filtering is based on the notion that decisions of viewers and purchases that have been made in the previous years can be used in collaborative filtering to obtain the nature and types of procedures that should be implemented in the international market (Robert 2014, 87). The technological applications, which are entailed in machine learning have been essential in making sure that the process has been implemented successfully by the company managers. Collaborative filtering helps in ensuring that the level of guesswork and errors have been reduced when carrying out a method that can create an immense in the outcome of an activity.

Machine learning also incorporates translation of diverse documents based on various rules that have been implemented by computational linguist and are versed with the languages that are being used for the process. The traditional translation procedure was found to have errors that included grammar and formatting based on the method that was involved in a guarantee that a document has been interpreted. However, with the introduction of machine learning, translated documents are retrieved with the help of technology and its applications, and the information obtained is used to create a comparison of the original document from the new one that has been interpreted. The process assists researchers to understand the primary measures that should be taken into consideration before any document has been translated into any language. For instance, retrieval of documents from the multilingual entities as well as the various proceedings from the Canadian parliament has been used as a reference of the regulations and requirements that should be followed by translators (Goodfellow, bengio, Courville and Bengio 2016, 54).


Machine learning has data types that are used in characterizing the problems encountered during learning, and the issues identified are used to develop solutions that can be used in either improving or solving the situation. The processes are undertaken by machine learning technologies assist in guaranteeing an encounter of new challenges and problems, which can be solved by the same techniques used by the technology application. For instance, bioinformatics and language processing are some significant tools for DNA sequences and natural language text. The basic entity of machine learning technology is vectors, and it has been applied by scholars and scientists in their field tasks and data analysis as well as processing. For instance, the life insurance organizations might have an interest in obtaining information and data, which are the vector of variables. The variables may entail gender, blood pressure, height, cholesterol level, heart rate, weight, smoker and height among others. The farmers, engineers and other practitioners in diverse fields also have variables that require the application of machine learning (Witten, Frank, Hall and Pal 2016, 32).

However, there are some challenges encountered when dealing with vectors, and they include the notion that units and scales used in diverse coordinates may have an increased variance. For instance, measurements of a ton, kilograms, and stones among other measurements might lead to several changes that are multiplicative being encountered by a researcher. Therefore, the problem can be solved by developing strategies that can assist in improving the condition, especially when a researcher is dealing with massive data and information in the field. Machine learning technology can be applied in handling the issue since it incorporates automatic fashion, which helps in normalizing data before its analysis (Robert 2014, 90).

Machine learning is important since it has helped in ensuring that Bayesian and data mining procedures have been made popular across the globe. There are various processes involved in data retrieval, computation and making them available, which have also been made powerful and cheaper as well as affordable. More complex data and information can be retrieved quickly and easily, which makes research studies and analysis an interesting activity. There are various problems that have been faced in the international market, and the aim of machine learning is making sure that the issues have been identified and resolved so that accurate results can be produced and automatically produced models can be adopted when analyzing complex and bigger data, especially when there is a need to deliver them faster. Company managers can adapt the machine learning applications and strategies by building up the precise models, which can be used in identifying opportunities that are profitable to the organization as well as reducing various risks that might be unknown to the employees and heads of departments in the working environment (Meng et al 2016, p. 1238).

There are some requirements that are needed for guaranteeing a good and proper machine learning system, and they include scalability, data preparation capabilities, iterative and automation processes, data presentation capabilities, and ensemble modeling. The processes involved in machine learning has been necessitated by the availability of technology, which has been essential in ensuring that researchers and scholars can gain access to various products and services quickly than when it was carried out traditionally (Robert 2014, 91).

The industries operating in the international market have been found to be working with huge and numerous data and information, which recognizes the use and value of machine learning technology. Over the years, it has been noted that company managers who have introduced machine learning technology in various organization activities have gained an increased competitive advantage over other organizations in the same industry. The banks and other financial services including businesses are some of the industries that apply machine learning technology with the aim of preventing fraud and identifying various insights of data (Goodfellow, bengio, Courville and Bengio 2016, 60).

The government agencies including the public utilities and safety have been found to have diverse sources of information, which can be collected for insights. In such instances, machine learning technology has helped in making sure that there is effectiveness in saving money, minimizing the rates of identity theft and fraud around the business environment. The other industries that have applied machine learning technology and strategies include marketing and sales, healthcare, transportation as well as oil and gas, and the various processes carried out by employees have been improving. The errors encountered during data collection, transfer and analysis have also been reducing over the years since the machine learning technology is also being developed by professionals.


Machine learning has been developed over the years with changes in technology with the aim of improving various activities undertaken by scientists and scholars, as well as company managers across the globe. The Internet and digital applications that have been adopted in the society have been influenced positively by machine learning technology. The application and data collection processes, which are involved in collection, analysis, and presentation, have also been made easy since the application provides various models that are reliable and effective.

Bayesian and data mining processes are some of the procedures that have been found to have been influenced positively by machine learning. The position of machine learning technology has improved in the recent years based on the fact that its impact has created an immense effect on the society and company managers are adapting the application in the activities undertaken in the business environment. The forms of machine learning have been used to improve business activities as well as other research study and analysis processes that are carried out by scholars and scientists across the globe.


Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M. and Kudlur, M., 2016, November. TensorFlow: A System for Large-Scale Machine Learning. In OSDI (Vol. 16, pp. 265-283).

Alpaydin, E., 2014. Introduction to machine learning. MIT press.

Ioffe, S. and Szegedy, C., 2015, June. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning (pp. 448-456).

Goodfellow, I., Bengio, Y., Courville, A. and Bengio, Y., 2016. Deep learning (Vol. 1). Cambridge: MIT press.

Meng, X., Bradley, J., Yavuz, B., Sparks, E., Venkataraman, S., Liu, D., Freeman, J., Tsai, D.B., Amde, M., Owen, S. and Xin, D., 2016. Mllib: Machine learning in apache spark. The Journal of Machine Learning Research, 17(1), pp.1235-1241.

Quinlan, J.R., 2014. C4. 5: programs for machine learning. Elsevier.

Robert, C., 2014. Machine learning, a probabilistic perspective.

Witten, I.H., Frank, E., Hall, M.A. and Pal, C.J., 2016. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.

September 04, 2023
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Artificial Intelligence

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