The most important part of Machine learning is data, we should be having the right with us to be successful with this. And also to train models, the data should be in the right format. The next question arises that how to get the right data, right? What are the ways in which we can get the right data? Well, getting the right data means collecting or identifying the data that correlates with the outcomes which need to be predicted. Or else we can say that data needs to be aligned with the problem we are trying to solve. Also, the data used to build the model should not be non-representative, error-ridden, and of low quality. Lets now see how this is done in machine learning.

Gathering Datasets for Machine Learning

Data collection is considered as the first level of the Machine Learning model building. Without data, there won’t be any machine learning and nothing would have been possible. The basic idea is that, the more the data, the more the accurate result is going to be generated. But remember, ‘more data’ does not mean a bunch of irrelevant data. We cannot add any data just to increase the quantity, the data we are using should be of relevant ones. So, we can say that any effort that is directed toward ‘finding the right data’ is well invested—that way after putting the collected data through a cleansing process, we will have ‘more data’ to build the model with.
Now, I am sure that you must be wondering how we can find the dataset for machine learning operations. There are two types of ways in which data can be collected, they are structured and the other one is unstructured one. Let us discuss in brief on what structured and unstructured dataset for machine learning is, for more information you can click here ml course.

Structured data are the ones which are organized and sanitized data which can be understood easily. Structured data can be displayed in rows and columns and, usually, it resides in relational databases, i.e. RDBMS.

Unstructured data can be textual or non-textual, human or machine-generated; it may also be in non-relational databases like NoSQL. It does not fit in relational databases, they can be text files, emails, etc. which are human-generated and hence requires more storage and time to parse it.