What is AI, Machine Learning, Deep Learning, and Data Science?
Tags: ai, machinelearning, deeplearning, datascience
Published: 2022-01-05 15:55:23

## What is AI? 
AI on its own really isn’t anything: just lines of code. When it comes to how that code is used in society and how those uses impact people, however—that’s when AI becomes more than just code. In The People’s Guide to AI (written by the fantastic Mimi Ọnụọha and Diana J. Nucera), AI is described as “like salt,” in that when it is added to a product, that product is transformed.

AI can be employed to help software and other tech products accomplish a wide variety of tasks, from sorting or locating information in a search engine to recommending music on Spotify. Forms of AI exist in public spaces such as schools, hospitals, and workplaces, as well as in the privacy of our homes.

To function, AI needs data. Data allows AI systems to build patterns, which they then use to generate predictions and simulations or to infer information about the world. Often, this data is gathered, extracted, and mined as a focused process to build an understanding of communities and of how we use technology.



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[![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/dh518zbwm1ub18004s64.png)](https://www.youtube.com/watch?v=16DNT-uUKfw) 



## What is Machine Learning?
Machine learning is a subset of artificial intelligence (AI). It is focused on teaching computers to learn from data and to improve with experience – instead of being explicitly programmed to do so. In machine learning, algorithms are trained to find patterns and correlations in large data sets and to make the best decisions and predictions based on that analysis. Machine learning applications improve with use and become more accurate the more data they have access to. Applications of machine learning are all around us –in our homes, our shopping carts, our entertainment media, and our healthcare.


## What is Deep Learning?
Deep learning is a branch of machine learning. Unlike traditional machine learning algorithms, many of which have a finite capacity to learn no matter how much data they acquire, deep learning systems can improve their performance with access to more data: the machine version of more experience. After machines have gained enough experience through deep learning, they can be put to work for specific tasks such as driving a car, detecting weeds in a field of crops, detecting diseases, inspecting machinery to identify faults, and so on.


## What is Data Science?
Deep learning networks learn by discovering intricate structures in the data they experience. By building computational models that are composed of multiple processing layers, the networks can create multiple levels of abstraction to represent the data.

For example, a deep learning model known as a convolutional neural network can be trained using large numbers (as in millions) of images, such as those containing cats. This type of neural network typically learns from the pixels contained in the images it acquires. It can classify groups of pixels that are representative of a cat’s features, with groups of features such as claws, ears, and eyes indicating the presence of a cat in an image.


## What is Data Science?
Data science is a multidisciplinary approach to finding, extracting, and surfacing patterns in data through a fusion of analytical methods, domain expertise, and technology. This approach generally includes the fields of data mining, forecasting, machine learning, predictive analytics, statistics, and text analytics. As data is growing at an alarming rate, the race is on for companies to harness the insights in their data. However, most organizations are faced with a shortage of experts to analyze their big data to find insights and explore issues the company didn’t even know it had. To realize and monetize the value of data science, organizations must infuse predictive insights, forecasting, and optimization strategies into business and operational systems. Many businesses are now empowering their knowledge workers with platforms that can help them conduct their own machine learning projects and tasks. Being able to extract trends and opportunities in the massive amounts of data being infused into a business will give an organization a competitive advantage.



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