Difference Between Data mining and Machine learning

Data mining: The process of extracting useful information from a huge amount of data is called Data mining. Data mining is a tool that is used by humans to discover new, accurate, and useful patterns in data or meaningful relevant information for the ones who need it. 

Machine learning: The process of discovering algorithms that have improved courtesy of experience derived data is known as machine learning. It is the algorithm that permits the machine to learn without human intervention. It’s a tool to make machines smarter, eliminating the human element. Below is a table of differences between Data Mining and Machine Learning: 

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S.No.	Data Mining	Machine Learning
1.	Extracting useful information from large amount of data	Introduce algorithm from data as well as from past experience
2.	Used to understand the data flow	Teaches the computer to learn and understand from the data flow
3.	Huge databases with unstructured data	Existing data as well as algorithms
4.	Models can be developed for using data mining technique	machine learning algorithm can be used in the decision tree, neural networks and some other area of artificial intelligence
5.	human interference is more in it.	No human effort required after design
6.	It is used in cluster analysis	It is used in web Search, spam filter, fraud detection and computer design
7.	Data mining abstract from the data warehouse	Machine learning reads machine
8.	Data mining is more of a research using methods like machine learning	Self learned and trains system to do the intelligent task
9.	Applied in limited area	Can be used in vast area
10.	Uncovering hidden patterns and insights	Making accurate predictions or decisions based on data
11.	Exploratory and descriptive	Predictive and prescriptive
12.	Historical data	Historical and real-time data
13.	Patterns, relationships, and trends	Predictions, classifications, and recommendations
14.	Clustering, association rule mining, outlier detection	Regression, classification, clustering, deep learning
15.	Data cleaning, transformation, and integration	Data cleaning, transformation, and feature engineering
16.	Strong domain knowledge is often required	Domain knowledge is helpful, but not always necessary
17.	Can be used in a wide range of applications, including business, healthcare, and social science	Primarily used in applications where prediction or decision-making is important, such as finance, manufacturing, and cybersecurity