Machine Learning is a subset of artificial intelligence that focuses on the development of algorithms and statistical models that enable Read more...
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This involves training models on a labeled dataset, enabling predictions or classifications based on input data.
In this paradigm, algorithms learn patterns from unlabeled data to identify structures and relationships within the dataset.
These models, inspired by the human brain, are used for handling complex tasks like image and speech recognition by learning hierarchical representations.
This topic covers techniques such as cross-validation and performance metrics to assess the accuracy and generalizability of machine learning models.
This involves the process of selecting, modifying, and creating features to improve the performance of machine learning models.
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Studying algorithms and statistical models that allow computers to perform specific tasks by learning from and making predictions or decisions based on data.
Exploring a subset of machine learning that models algorithms inspired by the human brain, used extensively in recognizing patterns and complex decision-making.
Understanding the technologies that enable computers to understand, interpret, and respond to human language in a useful way.