The beginner's guide to understanding data science and machine learning

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We are on the brink of a massive technological revolution as we slowly move from the first industrial revolution powered by water and steam to the fourth industrial revolution powered by artificial intelligence. The theories behind data science and machine learning have been around for hundreds of years. There was a time when protocomputers took almost forever to complete a billion calculations. Nobody dared to think about artificial intelligence or related technology. Thanks to machine learning and data science, we can now compute data at a capacity of 5 billion computations per second.

Data science and machine learning are some of the most popular disciplines that evaluate and analyze Big Data for beneficial purposes. Whenever big data or data in general is mentioned, our minds go straight to data science and machine learning. Although the two disciplines are significantly different, they have a unique and symbiotic relationship. This article will explain in detail the concepts of data science and machine learning, their special relationship and practical examples.

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Data Science

As mentioned above, our world is about to be invaded by data. Data quickly becomes overwhelming and tedious to manage. Tons and tons of data is generated every second. The advent of the Internet has pushed this development even further. Wherever you go, your data is knowingly and unknowingly collected, from gestures as simple as opening a door via fingerprint sensor automation to buying groceries at a grocery store .

Data science is the study of data and the processes involved in extracting and analyzing data for problem solving and predicting future trends. Data science is a broad discipline that is interconnected with other fields, such as machine learning, data analytics, data mining, visualizations, pattern recognition, and neuroinformatics, to name a few. to name just a few.

Data scientists study, analyze, infer, and present data that solves technology-related business problems. Data science draws inferences, interpretations, and conclusions from data that can be used for informed decision making. This science is built on fundamental disciplines such as statistics, mathematics and probability. In its entirety, data science is about understanding data and interpreting it.

machine learning

Machine learning studies data over time to create predictive models that can discern trends and solve problems without human intervention. Machine learning is a subset of data science. Using algorithms and development tools, machine learning engineers build expert systems that can learn to operate autonomously without human intervention. This is achieved through a series of algorithmic approaches divided into four categories: supervised, unsupervised, semi-supervised and reinforcement learning.

Machine learning engineers study big data to simulate machines that can behave and think like humans. Machine learning uses fundamental disciplines such as strong programming skills in languages ​​such as Python and R, as well as mathematics and data processing. Machine learning uses a lot of data; machines rely on this input to gain knowledge and understanding and also to act independently of human information after full simulation. Thanks to machine learning, artificially intelligent systems continue to grow in number as smarter agents are developed.

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