Data cleanup, or data rubbing, is the process of detecting and correcting or removing data or records that are incorrect from a database. It also includes correcting or removing unformatted or duplicate data or records. The data removed in this process is often referred to as “dirty data”. Data cleaning is a necessary process to protect data quality. Large businesses with extensive datasets or assets typically use automated tools and algorithms to detect such records and correct common errors (such as missing zip codes in customer records).
The most powerful big data circles have rigorous data cleanup tools and processes to ensure that data quality is protected and trust in datasets is high for all types of users.
Transfer Learning is a powerful technique used to speed up the training process and improve the performance of artificial intelligence and machine learning models. Transfer learning enables a model to reuse knowledge learned in a previous task in another task.
R is an open source programming language used for statistical analysis. Includes a command-line interface and various graphical interfaces.
Explore the world of Internet of Things (IoT), a powerful technology that is reshaping our lifestyles, work systems, and industries. Learn what IoT means, its applications, advantages and key role in driving the fourth industrial revolution.
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