Highlights the advantages and disadvantages of machine learning

Machine learning is a process where your system learns from the occurrences, experience and keeps in improving its skills and decision-making ability.

It is an Artificial Intelligence (AI), application learning skills by the system. It does not require our instruction to take decisions it keeps on learning itself.

Artificial intelligence solutions in the system help it to find it some sort of pattern in the data itself and from there it can perform its own task and make its decision taking ability eventually better for future purposes. The main objective of machine learning is to enable the system to take its decision automatically without any human interference, assistance or guiding the system to take precise or accurate decisions.

Artificial intelligence (AI), in the field of computer science AI is the term that actually perceives its environment. Basically, it helps a system to increase its work efficiency, thinking ability, decision-making ability and helps a system to work as a human with the help of machine learning.

Artificial intelligence development in the process of ML is really a progressive process.

Examples of machine learning are- medical diagnosis, image processing, regression, learning association.

Methods of machine learning

The machine learning process often follows two categories: supervised and unsupervised machine learning algorithms.

• Supervised machine learning algorithms

This algorithm helps to understand how the system has learned in the past and also at the present and also understand how accurate are the outputs for future analysis. They help in considering a dataset or say a training dataset, and then with the use of this algorithm, we can produce a function that can make predictions for the resulting outputs. Later the outputs will be checked for errors for more accurate results comparing it with the already calculated output initially.

• Unsupervised machine learning algorithm

This algorithm helps to check if the system can actually draw data and inferences from no resulted outputs and no information for the training. Now the system from the hidden structure and from all the relevant and several unused data draws a pattern to actually give details of the hidden structure. Here they give an output but it is not necessary to check whether the given output is accurate or not.

Machine learning is a process that enables the analysis of a large amount of data. It is a faster process in learning the risk factors, and profitable opportunities. They have a feature of learning from their mistakes and experiences. Even they can eliminate making errors on the same work for that it requires some time to understand the reason. When machine learning is combined with Artificial Intelligence and other cognitive technologies it can be a large field to gather an immense amount of information and then rectify the errors and learn from further experiences, developing in a smarter, faster and accuracy handling technique.

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Some of the advantages and disadvantages of machine learning

Every process or technique has some sort of pros and cons. Even in the case of machine learning, there are some factors that lead to advantages or disadvantages.

Let us first discuss the advantages of machine learning

• Identification of trends and patterns

Machine learning helps to manage a large amount of data and understand the trends and pattern that could have been not possible to manage that large amount of data by humans. For example- In the e-commerce industry like Myntra, it helps to understand and manage its marketing business by the user requirement. Like the deals, products, a number of clicks, offers, coupons and on the basis of all these options the business growth is eventually dependent.

• No human interference is required

Because of the machine learning technique, we don’t need to assist our system or give it commands to follow certain instructions. To control their decision-making ability. Rather let it take its own decision by itself without our interference. Hence it helps them to develop and improve their decision-making ability by themselves and also to rectify the errors.

Read the blog- How machine learning can be the perfect guiding light of enterprises

• Continuous improvement

Consisting of a machine learning algorithm it helps the system to continuously understand the errors and resulted rectification for that errors. Hence this increases efficiency and accuracy. For example- If we are designing a weather forecast application and it gives us regular weather predictions. The accuracy for that prediction depends completely on the regular error check and with improved accuracy.

• To handle the multi-dimensional and large amount of data

The machine learning algorithm helps in managing and improving the multi-dimensional and large amount of data and improving their skills in having no errors in them with the help of AI technology.

• Wide application

ML can be helpful for those who are in the field of e-commerce or the healthcare providers they can make use of ML to get immense help in their market growth and also it helps in the increase of the human work efficiency. The use of this application gives the customers a very personal experience to use this while targeting the right customers.

Disadvantages of machine learning

Some of the cons that are even faced commonly in the field of the machine learning process. Those factors that put an impact in ML are as follows:

• Data acquisition

In the process of machine learning, a large amount of data is used in the process of training and learning. So these use of data should be of good quality, unbiased. During the process of machine learning with help of software development services, there are also moments when we need to wait. In that period of time new data is being generated and can be used for further process.

• Time and resources

During the procedure of machine learning process the algorithms that help to manage all the functions to manage the data and use of certain data in the process of rectification if any errors this all requires time. And also trusted and reliable resources for the functioning of this system.

• Interpretation

When the algorithms help in all these processes and give a resulting output. This given output must be checked for any errors and the correction operation should be followed to get the desired accuracy. And during the selection of this algorithm, we must select that algorithm which you require for the purpose.

• High error susceptibility

In the process of machine learning, the high amount of data is used and on the other hand, many algorithms are used and tested. Hence there is a huge change to experience many errors. Because while you are training your dataset at that particular many algorithms is used if there is any mistake in the algorithm then it can lead the user to several irrelevant advertisements.

These blunders are a common issue that is experienced many times. Because when these mistakes happen, it is not easy to find out the main source for which the issue is been created and to find out that particular issue and rectifying it, takes a longer time.

Conclusion

In this fast living life, we need to manage all our work within a given time in this case if our system takes a few decisions to keep it updated with the resources is really necessary. Machine learning with the help of artificial intelligence solutions and other cognitive technologies makes it a new era in the field of development in computer science.