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Data science & machine learning are closely related concepts but with a different & specific purpose. The key objective of machine learning is to provide an implementation layer to artificial intelligence concepts. The speciality in machine learning systems as opposed to conventional applications is that ML applications are not extensively instructed by codes. Rather, they self-learn from the trends and patterns of data without any human intervention. For example – solutions such as smart homes, recommendation engines etc. are perfect examples of machine learning algorithms.
Data science is more intrinsically linked with extraction, cleansing and analysis of data to derive business insights out of it. There is definite overlap between the two as machine learning algorithms self-learn from data itself.
Data Science is a blend of Statistics, technical skills and business vision which is used to analyze the available data and predict the future trend.
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly