Naive Bayes classifiers are a family of simple probabilistic classifiers based on Bayes' theorem. They are highly scalable and require few parameters, making them efficient to train. They are also known as simple Bayes and independence Bayes.

Stanford University

Winter 2023

This course is centered on extracting information from unstructured data in language and social networks using machine learning tools. It covers techniques like sentiment analysis, chatbot development, and social network analysis.

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+ 14 more conceptsUC Berkeley

Fall 2022

UC Berkeley's CS 188 course covers the basic ideas and techniques for designing intelligent computer systems, emphasizing statistical and decision-theoretic modeling. By the course's end, students will have built autonomous agents that can make efficient decisions in a variety of settings.

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+ 20 more conceptsCarnegie Mellon University

Spring 2018

A comprehensive exploration of machine learning theories and practical algorithms. Covers a broad spectrum of topics like decision tree learning, neural networks, statistical learning, and reinforcement learning. Encourages hands-on learning via programming assignments.

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+ 55 more conceptsStanford University

Spring 2023

This course offers a thorough understanding of probability theory and its applications in data analysis and machine learning. Prerequisites include CS103, CS106B, and Math 51 or equivalent courses.

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