Advantages and Disadvantages of Machine Learning

In all the hype surrounding big data, we keep hearing the term “machine learning”. Not only does it provide high-paying careers, but it also promises to solve problems and benefit the company by making predictions and helping them make better decisions. In this blog, we will understand the advantages and disadvantages of ML. Because we will try to understand where to use it and where not to use machine learning. 

 So, let’s start with the advantages and disadvantages of machine learning. 

Advantages and Disadvantages of Machine Learning Language

Each coin has two sides, each with its own attributes and characteristics. It’s time to discover the face of machine learning. A very powerful tool has the potential to completely change the way things work. 

Advantages of Machine learning

• Easily identifies trends and patterns

Machine learning can view large amounts of data and discover specific trends and patterns that are not obvious to humans. For example, for an e-commerce site like Amazon, knowing the browsing behavior and purchase history of its users can help provide them with appropriate products, offers, and reminders. Use the results to show them relevant ads. 

Are you familiar with the application of ML? 

• No human intervention is needed (automation)

Using ML, you don’t need to pay attention to every step of the project. Since this means giving the machine the ability to learn, it allows the machine to make predictions and improve the algorithm itself. A common example is antivirus software; They learn to filter out new threats as they identify them. ML is also good at identifying spam. 

• Continuous Improvement

As ML algorithms gain experience, they continue to improve in accuracy and efficiency. This allows them to make better decisions. Suppose you need to make a weather forecast model. As the amount of data you have increased, your algorithm will learn to make more accurate predictions more quickly. 

• Handling multi-dimensional and multi-variety data

Machine learning algorithms are good at processing multidimensional and manifold data and can do so in a dynamic or uncertain environment. 

• Wide Applications

You can become a retailer or healthcare provider and let machine learning work for you. After application, it can help provide customers with a more personalized experience while targeting the right customers. 

Disadvantages of Machine Learning

With its powerful functions and all these popular advantages, machine learning is not perfect. The following factors are used to limit it: 

• Data Acquisition

ML requires a large number of data sets for training, which must be inclusive/unbiased and of good quality. Sometimes you may also need to wait for new data to be generated. 

• Time and Resources

ML needs enough time for the algorithm to learn and develop to sufficient accuracy and relevance to achieve its goals. It also requires a lot of resources to be effective. This may mean that you need additional computing power.

• Interpretation of Results

 Another major challenge is the ability to accurately interpret the results produced by the algorithm. You also need to choose the algorithm carefully according to your purpose. 

• High error-susceptibility

ML is autonomous, but it is error-prone. Suppose you use a data set small enough to train the algorithm so as not to include them. You will end up with biased predictions from the biased training set. This can result in irrelevant ads being shown to customers. In the case of ML, such errors may trigger a series of errors, and these errors may be ignored for a long time. When they are noticed, it takes a long time to identify the source of the problem, and even longer to correct it. 

SUMMARY

Therefore, we studied the advantages and disadvantages of machine learning. In addition, this blog can help people understand why it is necessary to choose machine learning. Although machine learning can be very powerful when used in the right way and in the right place (with a large number of training data sets), it is certainly not for everyone.

 

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