Introduction 

Machine learning vs Data mining. Our world is growing rapidly in the digital aspect and has popularised many new phrases and terms uncommon to a layman making him lose track and get overwhelmed. The same issue is being faced by the terms of machine learning and data mining. Although a very easy and effective concept, many people are not familiar with this convenience available in the digital world. If you are one of them, read on further to know what is machine learning and data mining, and what the difference between data mining and machine lea

Machine learning and data mining are two aspects of the concept of business intelligence. Business intelligence is a set of technologies, architectures, and processes that convert raw database into significant information. Business intelligence directly affects the decision-making process in an enterprise affecting all strategic, operational, and statistical decision-making. 

  1. What is machine learning?
  2. What is data mining?
  3. Difference between machine learning and data mining
  4. Machine learning vs data mining

1) What is machine learning?

Machine learning is the procedure of discovering algorithms improving the courtesy of experience derived from data. It is an aspect of artificial intelligence that provides the systems with the ability to improve and learn from experience without being explicitly programmed. Machine learning assists in developing computer programs that can access data by themselves and use it for their own purpose.

The main aim is to make computer programs improve and grow independently by learning automatically without human assistance or intervention. Machine learning helps enable the analysis of huge quantities of data. A model is built based on sample data, also known as training data to make predictions or decisions without being programmed to do so. In short, machine learning is the act and science of getting computers to do the work that a human would generally do. 

2) What is data mining?

Data mining is the process and practice of searching a large amount of stored data to discover trends and patterns going beyond simple analysis. Another name for data mining is knowledge discovery in data (KDD). In simpler words, data mining can be termed to extract useable data from a larger set of any raw database. This is done to establish relationships and identify patterns to solve problems through data analysis.

It is mainly used in machine learning, statistics, and artificial intelligence. Data mining was introduced in 1930 and involved finding hidden, useful, and valid patterns from large databases. Those questions are answered through data mining, which cannot be addressed through simple query and reporting techniques.

3) Difference between machine learning and data mining

Both machine learning and data mining fall under the concept of Data Science and are used for solving complex problems. Furthermore, both processes hire the same critical procedures for discovering data patterns. It is natural to confuse the two terms because of their similar patterns and behaviour, but machine learning and data mining essentially possess a couple of differences.

4) Machine learning vs data mining

  • Meaning: Machine learning means introducing a new procedure from data and experiences from the past while data mining is the process of mining knowledge from a large amount of data.
  • History: Machine learning was introduced in 1950 while data mining was started in the 1930s and was known as knowledge discovery in data.
  • Responsibility: Machine learning tutors the computer to understand and learn the given rules. Data mining discloses the rules from the existing data.
  • Origin: Machine learning includes algorithms as well as existing data. On the other hand, data mining unstructured data with traditional databases.
  • Implementation: Machine learning can be implemented in neural networks, decision trees, and many artificial intelligence areas while data mining can be used in credit risk management, database marketing, fraud detection and many more.
  • Nature: Machine learning does not require any human effort, is automated, and self-implemented, while data mining requires human interference and is dependent more on manual mode.
  • Application: Machine learning is applied in computer design, fraud detection, credit scoring, spam filter, web search and much more. Data mining is applied in cluster analysis.
  • Techniques involved: machine learning makes the computer self-learn to do the intelligent task while data mining involves more research using machine learning and more similar methods.
  • Scope: machine learning can be applied in a vast area, while data mining is restricted to a limited area.

The primary difference between data mining and machine learning is the use of human effort. As mentioned multiple times, machine learning is based on the principle of artificial intelligence, meaning getting a computer to do a human task. Data mining, on the other hand, involves more human work. Therefore the results which are produced by machine learning are more accurate as compared to data mining since machine learning is an automated process. Machine learning uses predictive models, neural networks and automated algorithms to get the work done. In contrast, data mining uses the data warehouse, the database server, and pattern evaluation techniques to extract useful information.

Conclusion 

Every day more and more part of our world is turning towards digitalising to improve the economy, solve problems and handle tasks more efficiently. Machine learning and data mining with the increased dominance of big data is today’s world is moving towards digitalising this world and ensure less and less human efforts. To drive a business into excelling and make proper decisions, both machine learning and data mining concepts are beneficial and have proven efficient and effective.

Both machine learning and data mining techniques go hand in hand as one defines the problem while the other gives an accurate solution. Machine learning is increasingly becoming a major career option with improved thinking and increased salaries. If you want to be a part of today’s dynamic and exciting world, one must definitely look into the scope of artificial intelligence and the tools it has to offer. After reading this article, we hope that you have a clearer and more solid understanding of the various aspects of machine learning and data mining and the role it plays in today’s digitalising world.

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional. 

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