In the past few years, the terms machine learning (ML) and deep learning have begun showing up frequently in many technology news and websites. The major difference between machine learning and other statistical methods, like data mining, is a popular subject of debate.

In laymen’s language, ML and data mining process use many of the same algorithms and techniques but one major difference lies in what the two methods predict. While data mining is used to uncover previously unknown patterns and knowledge, Machine learning is used to reproduce known patterns and knowledge.

Machine Learning

ML provides algorithms that resolve the problem based on the data, and the solution improves with time.

Examples:

  • Identifying new customers
  • Predicting lottery numbers
  • Detecting oil spills on sea surface
  • Assigning documents to a folder

Data Mining

Data Mining is used to extract regularities from a very large database as part of a business cycle.

Examples:

  • Fraud detection
  • Customer churn
  • Direct mail targeting

Advancements in technology and computing power of today's computers have helped data mining techniques evolve for use in machine learning. For example, neural networks are being used in data mining applications for years. With the help of more computing power, you can build neural networks with several layers. In machine learning, these are termed as deep neural networks. Increased computing power is the key to enabling automated learning in neural networks.

In addition to deep neural networks, there are a set of algorithms known as artificial neural networks that are based on our understanding of the brain. In theory, artificial neural networks can model any kind of association within a data set, but in reality getting reliable results from neural networks can be very challenging.

Currently, a new field of neural network research known as deep learning is used in areas where numerous artificial intelligence approaches have failed earlier. Deep learning combines computing power and various types of neural networks to study complex patterns in large amounts of data. At present, these are the advanced techniques that are used for recognizing objects in images and words in sounds. Researchers are now trying to apply these advanced techniques in more complex tasks such as medical diagnoses , automatic language translation, and various other social and business problems.

ML is generic in nature and finds its application in varied conditions. The importance of data mining is more on the utilization of known data obtained from a specific source. Sometimes, data mining is also used to improve the accuracy of machine learning algorithms.

Conclusion

While data mining is used to improve machine learning algorithms, ML provides a strong basis for understanding the basics of data mining. Both of them can be used effectively for cracking various types of complex problems. Both terms have strong similarities and these two terms would likely to exist together in future.