Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learning as a “Field of study that gives computers the ability to learn without being explicitly programmed”. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions.
Data Science isn’t fundamentally a new field. We’ve long had statisticians, analysts and programmers. What’s new is the way it combines several different fields into one. These fields are mathematics primarily statistics and linear algebra, computing skills like programming and domain knowledge along with communication skills. Data science is about predictions and associations but not causality.
So, we can easily say that data science and machine learning have a huge overlap and one can be used in place of the other. But, there are some differences between the two. Data science is a broader term which includes machine learning. Machine learning is a part of data science which deals with algorithms, statistics and structures of data to extract useful information from the data whereas data science is a multidisciplinary field which uses machine learning to extract knowledge and insights from the data.
Examples of some data science projects
- Determining which customers are most likely to repay a loan
- Determining the optimal coupon amount and timing for an ecommerce store
- Assisting a manufacturing company to reduce unplanned downtime by predicting machine failure before they occur
- Recommending which models of cars a rental car company should purchase to optimize profits
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