Similar to data science, artificial intelligence is a wide area of technologies, systems and more that seek to replicate human intelligence through machines.
Different parts of above pattern or loop are used by Data Science to solve specific problems. For example, in the first step, i.e. Perception, data scientists aim to identify patterns with the help of the given data. Similarly, in the next step, i.e. Planning, have two aspects:
- Finding all possible solutions.
- Finding the best solution among all possible solutions
Data analysis provides a framework that interrelates the above points as well as helps companies step forward. While machine learning can be clarified by taking it as a stand-alone topic, it can best be understood in the sense of its environment, i.e., the framework within which it is being used. To put it simply, machine learning is the bridge that ties Data Science and Artificial intelligence.
That is because it’s the process of learning from data over time. So for getting results and solutions for specific problems data science takes help from AI However, machine learning is what helps in achieving that goal. Google’s Search Engine is a real-life example.
- Google’s search engine is a product of data science.
- To deliver intelligent results to the users, it uses predictive analysis, a system used by AI.
- For instance, if a person types “best jackets in NY” on Google’s search engine, then the AI collects this information through machine learning.
- Now, as soon as the person writes these two words in the search tool “best place to buy,” the AI kicks in, and with predictive analysis completes the sentence as “best place to buy jackets in NY” which is the most probable suffix to the query that the user had in mind.
To be precise, Data Science covers AI, which includes machine learning. However, a Sub-technology Deep Learning also covered under machine learning. Deep Learning is a form of machine learning but differs in the use of Neural Networks where we stimulate the function of a brain to a certain extent and use a 3D hierarchy in data to identify patterns that are much more useful.
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Written by Prof. Yashvardhan Soni, Faculty Member, CSE Department