Advantages and Disadvantages of Machine learning: Today is the world of technology, where people are mostly dependent on artificial intelligence or machines for their work. From television to big supercomputers all are designed to make our life smoother and simpler. These all machines work on the basic principle of machine learning. Artificial learning has allowed people to make prompts and efficient decision-making. We can say machine learning is the key to analyzing data for decision-making. Let us look at what Machine learning is.
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What is Machine Learning? Advantages and Disadvantages of Machine learning 2022
Machine learning is a component of artificial intelligence. It is a study of algorithms of computers that is enhanced with experience and the use of data. It is used in a wide variety of fields like medicines, speech recognition, etc. A subset of machine learning is also statistical computation that focuses on making predictions using machines or computers. Today Machine Language is used mainly for two purposes I.e. for future prediction and to organize data based on the learning model. Machine learning flourished in the 1990s. Machine learning is extremely powerful thus must be used carefully according to its need. Therefore after learning its advantages and disadvantages one must use it for their benefit. Now let us look at the advantages and disadvantages of Machine language.
- Advantages of Machine Learning
- Disadvantages of Machine Learning
- Comparison Table for Advantages and Disadvantages of Machine Learning
- FAQs on Pros and Cons of Machine Learning
Listed are a few points for the advantages of Machine Learning. Let us briefly look at the advantages of Machine learning
- It is automatic: In machine learning, the whole process of data interpretation and analysis is done by computer. No men intervention is required for the prediction or interpretation of data. The whole process of machine learning is machine starts learning and predicting the algorithm or program to give the best result. One of the examples in the Google home that detect the voice and them accordingly finds out the result that the user wants, and antivirus software detects the virus of the computer and fixes it.
- It is used in various fields: Machine learning is used in various fields of life like education, medicine, engineering, etc. From a very small application to very big and complicated structured machines that help in the prediction and analysis of data. It not only becomes the healthcare provider but also provides more personal services to the potential customer.
- It can handle varieties of data: Even in an uncertain and dynamic environment, it can handle a variety of data. It is multidimensional as well as a multitasker.
- Scope of advancement: As humans after gaining experience improve themselves in the same way machine learning improve themselves and become more accurate and efficient in work. This led to better decisions. For example, in the weather forecast, the more data. And experience the machine gets the more advanced forecast it will provide.
- Can identify trends and patterns: A machine can learn more when it gets more data and since it gets more data it also learns the pattern and trend for example for a social networking site like Facebook people surf and browses several data and their interest is recorded and understand the pattern and shows the same or similar trend to them to keep their interest within the same app. In this way machine learning help in identifying trends and patterns.
- Considered best for Education: Machine learning is considered best for education as education is dynamic and nowadays smart classes, distance learning, and e-learning for students have increased a lot. Smart machine learning will act as a teacher and keep students updated with the current scenario of the world. The same thing happens in shopping or e-business people need to remain updated therefore they are shown the current trends of the world.
Listed are a few points for disadvantages of Machine Learning
- Chance of error or fault is more: Although machine learning is considered to be more accurate it is highly vulnerable. For example, a set of programs provided to the machine may be biased or consist of errors. The same program is used to make another forecast or prediction then there will be a chain of errors that could be formed which may, although recognized but take some time to find out the source of the error.
- Data requirement is more: The more data a machine gets the more accurate and efficient it becomes thus more data is required to input to the machine for better forecasting or decision making. But it may sometimes not be possible. Also, the data must be unbiased and of good quality. Data requirements are problematic sometimes.
- Time-consuming and more resources required: There can be times when the learning process of the machine may take a lot of time because the effectiveness and efficiency can only come through experience which again requires time. Also, the resources required are more for example additional computers may be required.
- Inaccuracy of interpretation of data: As we have already seen that a little manipulation or biased data could lead to a long drawn error chain and therefore there are chances of the inaccuracy of interpretation also. Sometimes data without any error could also be interpreted inaccurately by the machine as the data provided previously may not fulfil all the basics of the machine.
- More space required: As more data is required for interpretation more space is required to store the data which is one of the shortcomings of machine learning. More data means more knowledge or material to learn from for the machine, this requires a lot of space to store or manage data for further decision making.
After a brief discussion of the advantages and disadvantages let us go through the comparison table.
Given below is the comparison chart for the advantages and disadvantages of machine learning.
|Advantages of Machine Learning
|Disadvantages of Machine Learning
|It is automatic
|Chances of error or fault are more
|It is used in various fields
|Data requirement is more
|It can handle varieties of data
|Time-consuming and more resources required
|Scope of advancement
|Inaccuracy of interpretation of data
|Can identify trends and pattern
|More space required
|Considered best for Education
What is the use of Machine learning?
Machine learning is used in education, online shopping, decision-making processes, etc. They are the bright ideas of the intelligent mind.
What is Machine Learning?
Machine Learning is an algorithm of the computer that enhances the decision-making process. It allows users to take the advantage of prompt decision-making.
What is Artificial intelligence?
Artificial Intelligence refers to the ability of a machine or computer to behave, work and take decisions as a normal person performs. Although they cannot eat, drink, sleep and smell, their intelligence is the same as a human.