Techniques
In the context of Data Science and Artificial Intelligence, techniques refer to the various methods and approaches used to analyze, manipulate, and extract insights from data. These techniques can be broadly categorized into two types: supervised and unsupervised learning. Supervised learning techniques involve the use of labeled data to train a model to make predictions or classifications on new, unseen data. Examples of supervised learning techniques include linear regression, decision trees, and neural networks. Unsupervised learning techniques, on the other hand, involve the use of unlabeled data to identify patterns and relationships within the data. Examples of unsupervised learning techniques include clustering, principal component analysis, and anomaly detection. Other common techniques used in Data Science and Artificial Intelligence include data preprocessing, feature engineering, model selection and evaluation, and deep learning.
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- ETL: ETL stands for Extract, Transform, and Load. It is a process of extracting data from various sources, transforming it into a format that can be easily analyzed, and loading it into a target database or data warehouse. The ETL process is a c ... Read More >>
- Object Detection: Object detection is a computer vision technique that involves identifying and localizing objects within an image or video. It involves the use of deep learning algorithms to analyze and classify objects within an image or video stream. Obje ... Read More >>
- Secondary System: In Data Science, a Secondary System refers to a system that is designed to support the primary system by providing additional functionality or processing capabilities. The Secondary System is typically used to handle tasks that are not crit ... Read More >>
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