What are the Pros and Cons of Machine Learning you should Know
You may often wonder about machine learning. It is a process of learning providing the ability to the systems to learn and improve automatically. It would be done with experience without the need to program it specifically. It emphasizes on the development of computer programs accessing the data and using it for independent learning.
Machine learning has several benefits and drawbacks. Let us delve on the pros and cons of taking up machine learning as your career.
Benefits of machine learning
Find below a few essential benefits of machine learning you should be aware of before taking it as a career.
- Helps in identifying patterns and trends easily
It would entail reviewing large volumes of data for discovering specific patterns and trends not easily perceived by people. For instance, machine learning would relatively useful for an e-commerce website for understanding buying histories and browsing behaviors of the users. It would help you cater to the best deals, products, and reminders relevant to them.
- No need for human intervention
Machine learning would not require human intervention. It would provide the machines with the ability to learn on their own. It would help the machines make relevant predictions along with improving the algorithms independently or without human help. For instance, the anti-virus software would be a common example, as they would automatically filter the new threats as and when it is recognized.
- Provides continuous improvement
Machine learning algorithms would help improve the accuracy and efficiency with time and experience. It would help take better and informed decisions.
Drawbacks of machine learning
If you were contemplating taking up machine learning as a career, you should be aware of a few drawbacks associated with machine learning. It would help you make an informed decision.
- Requires massive time and resources
Machine learning would require a lot of time and massive resources to function properly. It could demand more computing power. You may need more time to let the algorithms learn and develop the purpose they have been created for with accuracy. It would fulfill your purpose in the best possible way.
- Massive datasets to train
Machine learning would require huge datasets for training purposes. It should be of good quality and unbiased. In a few situations, they might require waiting for the generation of the latest data.
- Accurate interpretation is challenging
Accurate interpretation of the results would be generated by the algorithms. However, it would be a daunting task nonetheless. You would be required to be prudent while choosing algorithms for their particular purpose.