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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