What is Machine Learning

Published Categorized as Artificial Intelligence
machine learning

Machine learning is a branch of artificial intelligence which actually show the intelligent human behavior. Artificial intelligence can make the complex tasks quite simple and thus creates possibilities for the humans. Apart from this, the procedures are less time consuming. As far as the ML is concerned, it usually show the behaviors like humans show. Machine learning can comprehend the scenarios more alike humans and hence it is possible to recognize a visual scene and as well as the written text. The machines can also perform an action in the physical world. The following are the types of machine learning.

Supervised Machine Learning

The specific datasets are labeled within the supervised ML model. The supervised strategy allows the models to learn and grow with the passage of time. For instance, dataset includes the pictures of dogs. The ML algorithms will learn new ways of recognizing the pictures with the passage of time.

Unsupervised Machine Learning

In supervised ML, any label doe not exist on the dataset. Thus the machine looks for the unidentified patterns.

Reinforcement Machine Learning

In reinforcement ML, it enables the machines to learn through trials and as well as the errors to perform a best action. The performance are based on right decisions.

Advantages of Machine Learning

There are always two faces of every blessing. It includes advantages and disadvantages. Lets uncover the advantages.

ML recognizes trends and pattern

ML can work more faster than the humans. However, its main purpose is still similar to humans. ML can review huge quantity of data and recognize any specific trends and patterns exists within the data. Hence the results achieved are quite faster and accurate thus increases the overall throughput rate.

Machine Learning is Automated

ML language is fully automated. It does not require human intervention in any task to carry out the operations. It works on the same principle as the anti-virus software runs. The virus detects any threats from the computer without any kind of human intervention. Likewise, the machine learning conducts the overall review of any task and act accordingly.

Constant Development

As human minds shows efficiency and accuracy as they gain experience, ML algorithms also do the same. Thus, they keep on improving their accuracy and efficiency as they gain experience. It is necessary to use the ML algorithms again and again to make them capable of showing high level of accuracy.

Handles multi-dimensional Data

The ML algorithms are quite good at handling the data thus machine learning is quite capable to respond to multi-dimensional data.

Disadvantages of ML

The disadvantages of machine learning includes

Data Accession

The data accession is really necessary thus make ML algorithms stronger. However, it requires huge quantity of datasets to train on. The data used should be good quality and there are many times when patience is required to generate the data.

Requires Time and Resources

The ML algorithms requires time and resources to enable the algorithms in learning so that they can give high level of accuracy. Apart from it, ML algorithms also require massive resources such as computer power to carry out the operations.

Evaluation of Results

Another biggest challenge involved in ML includes the evaluation of results. Apart from it, it is important to clearly evaluate the results generated.

High chances of Errors

Although ML algorithms do not require human intervention yet there are high chances of errors within the results generated. If you perform any kind of small mistake such as giving instruction not to be inclusive. Thus the ML algorithms will always strive for the biased data thus giving high chances of errors.

Hence, in order to be on the safe side, it is important not to give any kind of inclusive instruction to the machine. It will further remove the high chances of errors. Huge investments should be made on data accession so that the machines gain experience and able to perform better decisions.

References

https://mitsloan.mit.edu/ideas-made-to-matter/machine-learning-explained

https://data-flair.training/blogs/advantages-and-disadvantages-of-machine-learning/

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