Machine Learning datasets for Innovation and Progress
                     Machine Learning datasets for Innovation and Progress
”It is widely known that machine learning is as good as the data that we 
input in it. We often use an extremely large dataset to teach the 
machine learning model to differentiate between the identified 
datapoints.”
 
Before we go through training data, it is worth mentioning that in machine 
learning, there are three types of machine learning datasets: training, test, and 
validation.
If further classified, there are 2 different types of training data: 
Labeled data and unlabelled data.
 
Labeled data
Is used for supervised machine learning models. The data is tagged, labeled, or annotated by 
humans according to the defined criteria so that the particular machine learning model can 
produce the desired output. 
Labeled data also can even have more than one label depending on the set criteria.
For example, an image of a "drink can" could be assigned more than one tag; can, crushed 
can, drink can. This way, the machine is able to learn all the attributes of the particular image 
that are relevant to the model.
Unlabelled data
 
Is quite opposite of labeled data. We feed the machine learning model with raw data and let 
the model learn the pattern by itself. No human tagging is involved in unlabelled data.
If we used the drink example, then the model will evaluate the images based on 
their characteristics and in this case its shape. After dozens of images being fed 
into the model, the model should then be able to recognise the difference between 
those drinks.
There are also hybrid models which combine both supervised and unsupervised 
machine learning.
After learning the differences between labeled and unlabelled data now arises 
the question, 
"How do we know that our training data is GOOD?"
 
There are two important elements any good training dataset must have:
 
Relevancy
 
The data used must be related to the objective of the machine learning model and the items it 
learns from. You don’t want to use a picture of cars on a highway for your model to learn the 
differences between various types of drinks.
Focus on the dataset that’s related to your defined criteria. 
2. Consistency
With consistent data, You will likely have a high accuracy model in the testing phase. For 
example, the label used for specific characteristics is consistent throughout the entire dataset. 
This can be managed by simple tasks such as making sure the bounding boxes are always 
tight and the quality of the image is constant.
 
Employing these two methods would ensure high consistency and even higher accuracy.
Garbage in, garbage out
It is very easy and common to find low-quality data for a cheaper price or lesser resources. 
The question now stands, do you really want to feed this data to your machine learning or 
AI models, only to get inaccurate and inefficient results? 
The world of Artificial Intelligence very strictly follows the “Garbage in, garbage out” notion. 
That is why you may want to feed your machine only very high-quality data to obtain high 
accuracy output or result.
As of right now, there are lots of Machine Learning datasets that you can find 
online. So in case you want to train your model on specific cases, you might want to 
search it up online first before you start making your own dataset to save yourself 
some time.
 
 
Sourced from https://www.tictag.io/post/training-data-dataannotation-data-science