Data is the most important component of Machine Learning. In order to train models, we should have the ‘right data’ in the ‘right format.’ Now, you must be thinking how do we get the right data, right? Well, getting the right data means collecting or identifying the data that correlates with the outcomes which need to be predicted. In other words, 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. So let’s see how to get the right datasets for ML with ML Course.
In this module, we will be discussing the following topics:
- Gathering Datasets for Machine Learning
- Structured Dataset Vs. Unstructured Dataset for Machine
- List of Open-source Datasets for Machine Learning
Without more delay, let’s get started.
Gathering Datasets for Machine Learning
Data collection is considered as the foundation of the Machine Learning model building. Without data, the concept of building a Machine Learning model is futile. The more data we have the better the predictive model we can build out of it. But remember, ‘more data’ does not mean a bunch of irrelevant data.
We cannot add any data just to increase the quantity. 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 a dataset for machine learning operations. Dataset for machine learning can be found in two formats—structured and unstructured. Let us elaborate on what structured and unstructured datasets for machine learning is.
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Structured Dataset Vs. Unstructured Datasets for Machine Learning
Structured data is highly organized. It is comprised of clearly defined data types that are easy to digest. More importantly, structured data is easily searchable. Whereas, unstructured data, with no defined data types, is not easily searchable. The below image provides further differences between structured and unstructured data.
Structured data can be displayed in rows and columns and, usually, it resides in relational databases (RDMS). Data can be created by human or machine, as long as it is fit to reside in an RDMS, it can be searchable both by human-generated queries and by using algorithms using a type of data and field names. Typical structured data includes dates, phone numbers, credit card numbers, customer names, addresses, product names and numbers, transaction details, etc.
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. Human-generated unstructured data includes email text files, social media data, location-based data, and media files such as MP3, digital photo, audio, and video files. Typical machine-generated data includes weather data, surveillance photos, and videos, sensor-based traffic data, etc.
Structured data requires less storage space, which makes it easier to manage. But unstructured data requires more storage space.
According to Gartner, unstructured data makes up to 80 percent of the enterprise data. Unstructured data is growing in an insane manner. According to IDC, unstructured data grows at 26.8 percent annually compared to the structured data, which grows at 19.6 percent annually. Due to the sheer volume of the unstructured data, traditional data collecting techniques often leave out valuable information.
That is why unstructured data management needs to be different. Today’s enterprises need a separate data management platform that’s built specifically to handle unstructured data.
Know more from our blog on Datasets for Machine learning.