Machine learning is a new and modern technology that enables machines to interpret the data provided and draw useful conclusions with minimal human interference. As the name suggests, machine learning is the gradual process of learning by machines. First, a huge amount of data is provided to the software, and then it differentiates one type of data from another, recognizes patterns, and develops a conclusion. But as the charms of Machine Learning are numerous, like it saves human efforts and enables us to handle big enough data, which is impossible to do manually, it has some challenges in Big data analytics. They are listed below: –
Heterogeneous nature of data
There are various types of files in big data like audio, videos, speech, voice, etc. It becomes quite a difficult and power-consuming task for the machines to interpret any result based on this heterogeneously distributed data. Become a Data Scientist with 360DigiTMG Data Analytics Course in Chennai. Get trained by the alumni from IIT, IIM, and ISB.
Poor quality of data
If your training data has a humongous amount of error and noise, it will be very difficult for the machine to detect the underlying hidden pattern. Due to the poor data quality in Big Data analytics, the efficiency of machines to learn decreases drastically. This is why data scientists spend most of their time determining which data is of poor quality and which is of good quality. This screening is quite crucial. For example, you wish to draw inferences on the average sleep time of humans and give data on the sleeping of babies only to machines. Then the machine will not give useful output because the data provided is of poor quality as it doesn’t constitute the sleep time of adults and older persons.
Irrelevant and Useless features
If the training data provided to the machine is full of waste and useless data, then the machine will interpret the result as waste and useless only. For example, you want your machine to learn how an apple looks. If you provide the data of bananas, then the machine will never recognize an apple but will always consider it a banana as it has learned about bananas only. Want to learn more about data science? Enroll in the Data Analytics Classes in Pune to do so.
Biased Output
In Big Data analytics, sometimes the data is skewed toward a particular feature, and if this type of data is provided to the machine, then the machine will interpret and conclude biased results from this data. Suppose you provided the machine with beautiful models, expensive dresses, and extravagant makeup. And you asked the machine by giving the image of a village girl, whether she is beautiful or not? Under the influence of biased data, the machine claims NO. This is one of the biggest challenges of machine learning in Big Data analytics as it cannot differentiate between biases and unbiasedness. Earn yourself a promising career in data science by enrolling in the Data Analytics Course in Bangalore offered by 360DigiTMG.
Overfitting
Suppose once a stranger came to your office and asked for some money, stating the reason that he has lost his purse. You gave him some money, thinking it would help him reach home. But then you find out that it is his daily job to loot gullible people. So you assume the fact that all strangers are not worth helping, which is unethical and false. This same dilemma is called overfitting and overgeneralization. This happens in machine learning when the data provided is very complex, which is a common characteristic of Big data analytics.
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