Drolip | PROS AND CONS OF MACHINE LEARNING
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PROS AND CONS OF MACHINE LEARNING

PROS AND CONS OF MACHINE LEARNING

Machine Learning

Machine Learning is the process of making computers learn and act like humans, and improve their learning over time autonomously, by giving them data and information in the form of observations.

Pros of Machine Learning

Machine learning can be incredibly powerful when used in the right ways and in the right places, following are some points highlighting the advantages of Machine Learning.

  • Easily Identifies the problem and provides solution, Machine learning can revise large amounts of data and can easily detects the problem and the solution with the key words, that would not be so obvious and quick for the humans.
  • Best for examining large databases, when used in data mining, machine learning not only automates large data analysis, but also provides factual assumptions that can be used to support decisions.
  • Reduction of time and increases efficiency, machine learning ensures a reduction of the time cycle and efficient use of resources.
  • Improvement in result accuracy, as machine learning algorithms and patterns gain experience, they continue to improve accuracy and efficiency of final results. One good example of this is weather forecasting.
  • Can be used for multi purposes, Machine learning can be used for multi purposes at the same time. Such as compiling the data, responding to customers, organizing the data by filters while also targeting the potential customers.
  • Cost effective, as most of the tasks will be computerized, the cost for employment will cut down to half.

Cons of Machine Learning

Despite the many advantages, there are still risks and problems which are discussed below:

  • Identification of errors and correction, a notable limitation of machine learning is its sensitivity to errors. Diagnosing and repairing the errors can be difficult as it will have to undergo the complexity of coding and related processes.
  • Data accession, Machine learning requires a lot of training of data sets. Based on different algorithms, the data must be processed before it is entered as corresponding algorithms. This has a significant impact on the results to be achieved or obtained.
  • Consumption of time and resources, Machine Learning requires quite a time to allow algorithms to master and develop enough to fulfill their purpose with remarkable accuracy and relevance. It also requires large resources to operate. Therefore, a considerable time and capital is used to process machine learning.
  • Accuracy of final output, Machine learning systems may not always provide rational reasons for a particular prediction or decision. Moreover, these systems do not understand the context and answer the questions by identifying keywords, which increases the chance of errors. Human input is still important to better assess the performance of these systems.
  • Unemployment, However, machine learning systems will take over the job opportunities as much of the work will be done through systems without the human assistance which will eventually result in unemployment for some of the sectors.

Conclusion

There are different ways to develop and run a machine learning system. The advantages of a machine learning system depend on how it is developed for a specific purpose. In addition, the constraints of machine learning depend on the type of application or problem it is trying to solve. It is also important to note that these limitations are usually related to the quality of the data involved and the processing capabilities of the system.

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