Picking from where I left in the last post, I had explained 3 types of learning in ML, in this blog I will cover some more basic terminologies.
5. Types of Data : There are many types of data, but again to keep it simple and focussing on the basics, I will be explaining two categories of data. Structured vs Unstructured data , Quantitative data vs Qualitative Data
5.1 Structured vs Unstructured Data
5.2 Qualitative vs Quantitate Data
6. Deep Learning is a technique specifically used for analysing unstructured data, to make it simple, I would say DL is a modern ML technique based on Artificial Neural Network, all thanks to every increasing computational power.
7. Data Science is an art of extracting insights from data which can be help in making confident business decision. If you are data scientist, you will be helping the senior management identify whether to launch electric toothbrush in Iraq, or should they be launching sleep pills in americas.By helping I mean, data science sits at the intersection of math, programming, statistics and business.
There are innumerable number of terminologies like CNN, RNN, GAN, Boosting and the list can keep going on to make it a dictionary, but I think if you have understood terminologies mentioned in these two blogs, I believe you can now take the next step of Identifying how to chose an algorithm for a given problem.
Now once we have understood the terminologies, we can move to understand which are the problems AI can solve and algorithms used to solve the problems.
Shall soon be releasing that blog post, currently working on it :)
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