Data and its types

Recap of Previous Blog.

In my previous blog 'How Machine Learns' we talked about few examples of Machine Learning and came to the conclusion that, Machine Learns through going over lots of data this process is also called Training the Model (Model is the Machine Learning Algorithm).

But What is this Data?

The type of data is divided into many types lets talk about some of the types:

1. Structured Data

Structured Data are highly organized data which are in the form of tables. The columns in the table of these data represents features (or attributes) and the rows in the data represents an individual instance.
These types of data are used in Algorithms like Classification, Regression or Clustering (Ill be talking about these Algorithms in future blogs)
These data are usually CSV, Excel files, SQL database etc. formats.

*Example : *

In here the columns like 'age', 'income', 'loan_amount', etc. are the features of the data and the rows are the instance or the actual data of the table.

2. Unstructured Data

Unlike Structured Data the Unstructured Data does not have a predefined structure. This type of data is raw. Example : Text data, Images, Videos, Audios etc.
These types of data are used in Algorithms like NLP (Natural Language Processing), Computer Vision, Speech Recognition, etc.
These data are in Plain text, JSON, Image Formats (JPEG, PNG), Video Formats (MP4, AVI), Audio Formats (MP3, WAV) etc.

Example : (Image Data)

3. Semi-Structured Data

Semi-Structured Data is a hybrid type of data that contains some level of organization but doesn't follow a strict schema (or representation). It uses tags or markers to separate data elements.
Formats like : JSON, XML, YAML etc. are used in these type of data.
It is used for Web Scraping, IOTs, APIs, etc.

There are other types of data but we typically only use Structured and Unstructured Data in Machine Learning.

So what's next after getting the data?

Once the data is collected/downloaded we can then use the data to predict values using Machine Learning Methods/Algorithms by giving (feeding) the data to the Machine it will then learn (train) using that data.
I will talk about these Methods in the next Blog which will be about 'Supervised and Unsupervised Learning'.

Thanks for reading the blog see you guys in the next one.
If there is something you would like me to change or if I wrote something wrong be sure to notify me as it is my first blog and I am still learning about Machine Learning.