Data Scientist vs Machine Learning vs Artificial Intelligence vs Deep Learning
This is part of The ULTIMATE Curriculum in Data Science which you can refer for more information in Data Science.
I visualize a time when we will be to robots what dogs are to humans, and I’m rooting for the machines. - Claude Shannon
In Data Science you will see many terms such as Data Scientist, Machine Learning,Artificial Intelligence and Deep Learning. So what the heck is this?
In short, all this terms are child's of Data Science only. So if you want to learn or master in any of this field, you will have to understand Data Science.
This image explains clearly the scope of all of this fields in terms of Data Science.
This aspect of Data Science is all about uncovering findings from data. Diving in at a granular level to mine and understand complex behaviors, trends, and inferences. It’s about surfacing hidden insight that can help enable companies to make smarter business decisions.
Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.
Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:
- Problem solving
- Ability to manipulate and move objects
Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious task.
Machine learning is a part of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions. Classification determines the category an object belongs to and regression deals with obtaining a set of numerical input or output examples, thereby discovering functions enabling the generation of suitable outputs from respective inputs. Mathematical analysis of machine learning algorithms and their performance is a well-defined branch of theoretical computer science often referred to as computational learning theory.
Machine perception deals with the capability to use sensory inputs to deduce the different aspects of the world, while computer vision is the power to analyze visual inputs with a few sub-problems such as facial, object and gesture recognition.
Deep Learning is a sub-field of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.
The core of deep learning is that we now have fast enough computers and enough data to actually train large neural networks.
Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design and board game programs, where they have produced results comparable to and in some cases superior to human experts.
So in this chapter you have clearly understood the differences between Data Science, Artificial Intelligence, Machine Learning and Deep Learning.
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Happy coding :)
And Don’t forget to clap clap clap…
This is the first of a multi-part series explaining the fundamentals of deep learning by long-time tech journalist…blogs.nvidia.com