Machine Learning has been booming these days, with its applications now in various industries, like transport, e-commerce, healthcare, entertainment, finance, marketing, etc. It is revolutionizing many industries, and contributing to huge development and research in various industries.
If you are somehow familiar with the term Machine Learning, you might also have heard about something called Data Mining. If you have read about Data Mining earlier, you might have thought that in many cases, these terms seem to be similar. It might leave many people confused, so understand this, in this article, we are going to understand what is Data Mining, what is Machine learning, and also, some points of differences between Data Mining and Machine Learning.
Difference Between Data Mining And Machine Learning
What is Machine Learning?
Before we move to discuss what is Data Mining, and the points of differences, it is important for us, to discuss in brief about Machine Learning, so that in case if you are not familiar with the term at all, you would get a gist of it.
So, we can understand Machine Learning as something that enables machines to learn from the data, improve, and make predictions, or take decisions on the basis of that learning, without being explicitly programmed.
Explaining Machine Learning in a few words won’t be enough, and you might want to dive into more details to understand this interesting technology, but I think the above super brief explanation of Machine Learning would be able to give a broad picture of what is Machine Learning.
In other words, you can understand this as if machines are being fed with data, and they are learning from the data, and improving, and taking some decisions, making some predictions, without being explicitly programmed.
Machine Learning has been helpful to humans in many terms, and in many cases today, we experience/use Machine Learning in our daily life. So now, let’s understand what is Data Mining.
What is Data Mining?
Before understanding Data Mining, let’s understand what is Mining! Well, you might be familiar with the word already, but let’s try to build a general meaning of Mining. You might have heard about gold mining or diamond mining, so, in general, through mining, we are extracting valuable objects/materials from Earth.
So, in the case of Data Mining, we can understand this as if we are extracting valuable information, and useful patterns, from large datasets. It is also known as Knowledge Discovery in Data(KDD).
Through Data Mining, we can either describe the dataset, or we can also predict the outcomes by using Machine Learning algorithms.
You can explore more about Data Mining, but I think this is enough to have a broader view of understanding what is Data Mining.
Difference between Data Mining, and Machine Learning
Now, that we have got a super brief understanding of what is Machine Learning, and what is Data Mining, now it’s time to move towards some points of differences between Data Mining, and Machine Learning.
If you observe carefully, Machine Learning and Data Mining appear to be quite similar, because there are many similar things happening in both cases, like discovering patterns and extracting useful information from the data, but there are some differences between them, and we are going to discuss the same now –
Data Mining vs Machine Learning
| Data Mining | Machine Learning |
|---|---|
| Data Mining primarily focuses on finding out hidden patterns, extracting useful information from the large dataset | Machine Learning primarily focuses on creating algorithms and models, which can learn from the data, make predictions, and take decisions without being explicitly programmed. |
| Data Mining finds its use more in case of research or understanding the data, collecting data relevant to some specific domains. | In the case of Machine learning, there are various applications, like self-driving cars, image recognition, speech recognition, etc. |
| The scope of Data mining is limited to finding hidden patterns in the data and extracting some useful information for the businesses, but there can be more human intervention. | The scope of Machine learning is wider since the algorithms can learn by analyzing extensive volumes of data. Also, they can predict outcomes on future data. |
| Usually, the ultimate goal in Data mining is to extract useful information and uncover hidden patterns from the given historical and current data. | In Machine Learning, the goal is to train the ML algorithms to learn from the data, to make predictions, or take decisions without being explicitly programmed. |
| Data Mining often requires more human Intervention in comparison | Machine Learning requires very less or no human intervention. |
| Primary techniques involved in Data Mining – Association, prediction, classification, clustering, regression, and sequential Analysis. | In Machine Learning, the primary techniques involved are Supervised Learning, Unsupervised learning, and Reinforcement learning in order to improve on the existing analysis without human intervention. |
Conclusion
In the above article, we have seen some points of differences between Data Mining, and Machine Learning. These two terms might seem to be similar since there are many similar things involved, but still, there are some differences in the approaches, goals, techniques, and other things. I hope that the above-discussed points help you understand the simple points of differences between Data Mining, and Machine Learning.
Machine Learning has been booming these days, and if you are interested in knowing more about Machine learning, you can explore our articles related to Machine Learning, and also you can read about the Python programming language, which is one of the most popular programming languages in the world, and when it comes to Machine Learning, python is very popular.
FAQs related to Data Mining, and Machine Learning
Q: What is Machine Learning?
Ans: Machine learning can be understood as something that gives power to machines, to learn from the experience and improve, make predictions, or take decisions, without being explicitly programmed.
Q: What is Data Mining?
Ans: Data Mining can be considered as a process of understanding the data, extracting some useful information from the data, or uncovering some hidden patterns from the data, for the business. It generally focuses on extracting some useful information from the data.
Q: Which is better, Data Mining or Machine Learning?
Ans: Machine learning and data mining, might seem very similar, but the thing is that both differ in the approach and the ultimate goal. So, instead of thinking about what is better than the other in general, you should decide for yourself. If you are more interested in understanding the data and gaining insights from the data, then you should go for Data Mining, or if you are more interested in studying and developing algorithms that learn from data, make predictions, or take actions, you can go for Machine learning.
Q: When is Data Mining used?
Ans: Data Mining is usually used for research purposes, like when we need to understand the data, extract some useful information from the data, and uncover some hidden patterns from it.
