Blog tutorial for the different data processing techniques for different types of data
Here I will be posting the tutorials related to "How to process data?(rather different kinds of data)" such that your data is better structured to apply Machine Learning model to it.
Machine Learning has created a great buzz in the whole tech world. Many new technologies are using AI and ML for their betterment. But do you know that before applying these machine learning models on the data, the data needs to be processed well so that all the information from the data come out well, to be presented to the machine learning model. Even the most powerful Deep Learning model wouldn't be able to perform that well if the data provided to them are not processed properly.
Analogy: Let's say you have got a set of paragraphs and you are asked to tell "what are the topics this paragraph is exactly on?". Do you think you would be able to tell answer this question easily? But let me say now this whole set of paragraph has been properly structured, the keywords have been extracted from the paragraph and many other transformations have been brought to the paragraphs(for getting more information out of it). Don't you think that now you would be able to answer the questions easily?
Now let's consider another scenario, sometimes the data can't be directly presented to the model, in that case too we have to modify the data so that the data can be then sent as input to the model.
Analogy: Let's say you are in Rick's world and someone says to you "Wubba lubba dub dub", you won't be able to understand this but if you are told the meaning of each word of what he spoke, you would be able to unite them and come to a conclusion of what this person is talking about.
And hence most of the data scientists and ML engineers say that 80-90% of the time goes in preparing the data
Stay Tuned!! Tutorial Number 2 is on the way 🔜