76 results for "pattern recognition"

Pattern recognition is the automated recognition of patterns, or repeated designs, in data. It is a part of data mining and consists of multiple mining patterns. Pattern recognition is used for data streams, software bugs, and image analysis. It is also a big part of biological and biomedical studies for patterns of behavior in patients or image analysis like an MRI.‎

If you have a passion for data mining or want to get into a biomedical career like neuroimaging, learning about pattern recognition will help you dive deeper into pattern discovery and help you explore other areas of interest. Learning about pattern recognition will help you understand data-driven mining and other applications of pattern discovery, which is useful for interpreting real-world data and imaging.‎

Online courses on Coursera will help you gain an understanding of various areas of data mining including pattern recognition. The instruction provides you the opportunity to learn the necessary skills to practice and progress in pattern recognition. These courses go into numerous topics, helping you learn in-depth material to propel you forward in your career or help you pursue a new area of interest. Online courses on Coursera give you the flexibility to work at any time, and they have experts ready to guide you along as you progress through the course.‎

You can put your skills and knowledge learned about pattern recognition into a career in computer science or data analytics. With these skills, you'll be able to apply them to improving software and apps to better their performance and problem solve using various methods. You can also go into marketing analysis to understand consumers' behavior and help recognize business opportunities. Another career where pattern recognition is used is in the area of neuroscience to look at brain patterns and read different images like ones from an MRI. In this career, you will use your skills to identify where there is a mishap in the brain's function to help diagnose patients.‎