Machine Learning helps the companies to derive more accurate data which allows them to take better decisions. Proper approach to Machine Learning also enables the organization’s to address the problems and errors that the early traditional approaches couldn’t. But we should also know that Machine Learning is not some sort of magic and it too has some problems that need to be addressed.
In this article we are going to focus on the common mistakes of machine learning and also know how to fix those mistakes.
• A MACHINE LEARNING PROGRAM SHOULD NOT BE PLANNED WITHOUT DATA SCIENTISTS: The most common mistake in Machine Learning is that many times it is planned without a data scientist. Deep analytics knowledge is a crucial part in Machine Learning. In this case, employees with good analytical knowledge becomes more important. So, the organization’s should put more focus in employing the employees with good knowledge of Machine Learning and also of analytics. Furthermore when the data scientists are being considered then it should be known that they come with a high price tag and require engaging projects.
HOW TO FIX THIS ISSUE:
1 ) New interns should be recruited as much as possible by the organization’s. They should especially recruit interns who have a good knowledge of Machine Learning and who can help the company to solve their business problems.
2 ) If the data scientists are not available then make the analytics more approachable that is – the data visualization tools should be simpler and user friendly. It will help in problem solving to a great extent.
3 ) It should be enquired from the already existing employees that who has a taste for analytics and is interested. Those who are interested will surely give their best to help the company come out from the problem.
• INEFFICIENT INFRASTRUCTURE FOR MACHINE LEARNING: Machine Learning requires an efficient Infrastructure and if the companies fail to maintain it then it may lead to a problem for the organization’s. Managing and maintaining the right infrastructure for some companies is challenging. Whereas the amount of data the organizations collect today may lead to a lot of load and the management systems may fail.
HOW TO FIX THIS ISSUE :
1 ) The computation should be powerful, scalable and secure. It will help the data scientist to find the best possible solution with a reasonable amount of time by looking through the various preparation techniques and different models.
2 ) Elasticity is important as in Machine Learning the consumption of compute resources and the storage can be highly dynamic. The elasticity of the infrastructure allows the more optimal use of the limited resources of computation and / or expenses as well.
• DATA QUALITY ISSUES: It’s time that the truth is brought to the limelight. A good amount of time is needed to prepare data and dealing with data quality issues. Moreover the data quality varies from model to model. Some data quality issues include – noisy data, dirty data, sparse data and I adequate inadequate data.
HOW TO FIX THESE ISSUES:
1 ) Data should be integrated and prepared that is even after the data is prepared, collected and cleaned it should be transformed into a language which is logical for the Machine Learning Algorithm to understand.
2 ) Data should be explored that is the data scientist should be able to summarize and visualize data before and after the machine learning models are trained.
• IMPLEMENTATION OF MACHINE LEARNING WITHOUT A STRATEGY: It is an overall difficult task to incorporate newer and complex models.
HOW TO FIX THEM:
1 ) When a single machine algorithm fails to work several algorithms should be used.
2 ) Apply different models to each segment and factory approach to different models.
Effective use of machine learning helps to understand the basic concepts of broader analytics environment. Through this article you have known the common mistakes of machine learning and also how to fix these issues. Now you will be able to deal with these issues in a better away and also enhance the company's infrastructure for machine learning.