Machine learning is a technology by which machine can give us useful result from raw data. The data is given to machine in different ways i.e. previous data and current data. Suppose a weather forecast system uses machine learning to guess temperature in upcoming days. Weather system uses previous historical data and continuous data of current days to guess the temperature.
Future scope of machine learning:-
Machine learning (ML) is used in various fields like medical, weather forecasting, banking, retailing, online shopping, financial sector, ads suggestion by Facebook and Google, gaming, etc. ML has a bright future as it is giving positive results to different companies.
Let me discuss the different pros and cons of machine learning.
Advantages of machine learning (ML):-
Get trends ideas:
ML gets users data and shows him related products. Shopping website gets users data and what activity users performs on their site. Example of such website is amazon.com
Improve automatically:
ML does not involve programmer interaction and it improves as it gets more data. For example, the antivirus program continuously checks which virus in newly coming and it saves us from it.
Artificial intelligence:
ML works with AI to give good results. ML keeps learning from different data sets and builds intelligence in it.
Disadvantages of machine learning (ML):-
Takes time and high resources:
Ml does not give accurate results instantly but it takes time. As ML keeps learning and needs to process data continuously so it involves a lot of machine resources.
A lot of data needed:
ML needs a lot of data to give appropriate results. ML needs to get training from the initial data and then start working on future data.
Sometimes gives error:
ML works on the basis of algorithms that it uses. If we apply the wrong algorithm then it gives us errors and generates wrong results.
Costly:
For small companies, ML is not ideal as it needs more budget.
Conclusion of machine learning:-
Machine learning (ML) has a bright future as it is generating good revenue to various online and offline businesses.
