Difference Between Data Mining Vs Machine Learning Vs Artificial Intelligence Vs Deep Learning
What is the Difference Between Data Mining and Machine Learning Vs Artificial Intelligence Vs Deep Learning Vs Data Science, Read Here:
Data mining and machine learning are areas of inspiration, but they have much in common, but different goals.
Data mining is performed by humans on a specific data set to discover interesting patterns between elements in the data set. Data mining uses techniques developed through machine learning to predict outcomes.
Machine learning, on the other hand, is the ability of computers to learn from mined data sets. A machine learning algorithm builds a model to predict future outcomes by taking information that represents relationships between elements in a data set. These models are just the actions the machine takes to get results.
Difference Between Data Mining and Machine Learning
|Basis for Comparison
|Extracting information from a large amount of data
|Introducing an algorithm from data as well as past experience
|Introduced in 1930, Earlier known as knowledge discovery in databases
|Introduced in near 1950,ML first program was Samuel’s checker-playing program
|Data mining is used to get the rules from the given or existing data.
|Machine learning teaches the computer to learn and understand the existing rules.
|Traditional databases with unstructured data
|Existing data as well as Algorithms.
|We can develop our own models where we can use data mining techniques for the Dataset
|We can use machine learning algorithm in the Decision tree or Regression, neural networks and some other area of artificial intelligence techniques
|Involves human interference more towards manual efforts.
|Automated, once designed is self-implemented, no need of human effort
|used in Analysis of data like clusters
|used in spam filter, credit scoring, fraud detection, computer design, recommendation systems etc.
|Data mining is abstract from the data warehouse
|Machine learning reads a machine
|Data mining is more of research and information gaining using methods like machine learning
|Self-learned and trains system to do the intelligent task if applied advanced techniques.
|Scope is limited
|Scope is very wide and applied in almost every field.
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