Data Mining and Machine Learning – 4 Differences You Need To Know
It’s not at all an easy question to answer about the relationship or difference between machine learning and data mining. Data mining isn’t an invention that came with the progression of the digital age. The concept of data mining has been around for more than a century. But with broader applications and more widespread recognition; it grabbed the limelight in the late 1930s.
While both Data Mining And Machine Learning are entrenched in the modern data science and generally categorized under the same umbrella; but there are few points which differentiate them from each other. Here’s a quick look at some machine learning and data mining differences for aspiring data scientists.
Data Mining vs. Machine Learning
Nitty-Gritty Of Data Mining
Data mining is defined as the process of extracting knowledge from a whole host of data for developing descriptive or predictive models.
Data mining was initially defined as knowledge discovery in the database and was introduced in the 1930s.
The primary aim of data mining is to extract rules from the existing data.
Data mining can be used for extracting data from our own models.
Points About Machine Learning
Machine learning is the process of introducing a new algorithm from new data or from past experience.
Machine learning came into limelight around 1950, and the first program was named as checker playing program.
Machine learning is used to train computers to learn and identify with the rules.
Machine Learning Regression can be used in AI neural networks, decision trees, and some other areas of Artificial Intelligence.
