Advantages and Disadvantages of Machine Learning Language

1. Objective

In this blog, we will learn Advantages and Disadvantages of Machine Learning. As we will try to cover all Limitations and Benefits of Machine Learning to understand where to use it and where not to use Machine learning.

Advantages and Disadvantages of Machine Learning Language

2. Advantages and Disadvantages of Machine Learning Language

a. Advantages of Machine learning

i. As machine learning has many wide applications. Such as banking and financial sector, healthcare, retail, publishing etc.

ii. Google and Facebook are using machine learning to push relevant advertisements. That advertisements are based on users past search behavior.

iii. Machine learning is used to handle multi-dimensional and multi-variety data in dynamic environments.

iv. Machine learning allows time cycle reduction and efficient utilization of resources.

v. If one wants to provide a continuous quality, large and complex process environments. There are some tools present because of machine learning.

vi. As there are too many things that come under practical benefit of machine learning. Also, they involve the development of autonomous computers, software programs. Hence, it includes processes that can lead to automation of tasks.

b. Disadvantages of Machine Learning

i. Machine learning has the major challenge called Acquisition. Also, based on different algorithms data need to be processed. And, it must be processed before providing as input to respective algorithms. Thus, it has a significant impact on results to be achieved or obtained.

ii. As we have one more term interpretation. That it results is also a major challenge. That need to determine the effectiveness of machine learning algorithms.

iii. We can say uses of machine algorithm is limited. Also, it’s not having any surety that it’s algorithms will always work in every case imaginable. As we have seen that in most cases machine learning fails. Thus, it requires some understanding of the problem at hand to apply the right algorithm.

iv. Like deep learning algorithm, machine learning also needs a lot of training data. As we can say it might be cumbersome to work with a large amount of data. Fortunately, there are a lot of training data for image recognition purposes.

v. One notable limitation of machine learning is its susceptibility to errors. Brynjolfsson and McAfee said that the actual problem with this inevitable fact. That when they do make errors, diagnosing and correcting them can be difficult. As because it will need going through the underlying complexities.

vi. There are fewer possibilities to make immediate predictions with a machine learning system. Also, don’t forget that it learns through historical data. Thus, the bigger the data and the longer it needs to expose to these data, the better it will perform.

vii. Lack of variability is another machine learning limitation. Brynjolfsson and McAfee said that machine learning deals with statistical truths. In situations where ML is not included in the historical data, it will be difficult to prove. That the predictions made by this system are suitable for all scenarios.

3. Conclusion

As a result, we have studied Advantages and Disadvantages Machine Learning. Also, this blog helps an individual to understand why one needs to choose machine learning, benefits of Machine Learning, and limitations of Machine Learning.