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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-machine-learning-naive-bayes-in-python/
课程评论：没有评论
课程名称：数据科学与机器学习：Python中的朴素贝叶斯 概述： 在这门自学课程中，您将学习如何将朴素贝叶斯算法应用于多个真实世界的数据集，涉及的领域包括计算机视觉、自然语言处理、金融分析、医疗保健和基因组学。朴素贝叶斯是机器学习、数据科学和人工智能中的基本算法之一，深入掌握它是每位从业人员的必备技能。 课程适合所有水平的学生，无论您是初学者、中级还是高级学习者。您将学习朴素贝叶斯的工作原理及其有效应用，同时考虑到朴素贝叶斯算法的独特特性。课程将涵盖在Scikit-Learn中使用的不同版本的朴素贝叶斯算法，包括高斯朴素贝叶斯（GaussianNB）、伯努利朴素贝叶斯（BernoulliNB）和多项式朴素贝叶斯（MultinomialNB）。 在课程的高级部分，您将深入了解朴素贝叶斯的内部原理，并学习如何从头实现多种朴素贝叶斯变体，包括高斯朴素贝叶斯、伯努利朴素贝叶斯和多项式朴素贝叶斯。高级部分需要一定的概率知识，请做好准备！ 建议的先决条件： - 较好的Python编程技能 - 熟悉数据科学库，如Numpy和Matplotlib - 高级部分需具备概率知识 课程的独特特点包括： - 每行代码都详细解释，您可以随时通过电子邮件与我讨论 - 平均响应时间不到24小时 - 不怕大学级数学，提供其他课程中遗漏的重要算法细节 感谢您阅读此内容，期待您的参与！
In this self-paced course, you will learn how to apply Naive Bayes to many real-world datasets in a wide variety of areas, such as:computer visionnatural language processingfinancial analysishealthcaregenomicsWhy should you take this course? Naive Bayes is one of the fundamental algorithms in machine learning, data science, and artificial intelligence. No practitioner is complete without mastering it.This course is designed to be appropriate for all levels of students, whether you are beginner, intermediate, or advanced. You'll learn both the intuition for how Naive Bayes works and how to apply it effectively while accounting for the unique characteristics of the Naive Bayes algorithm. You'll learn about when and why to use the different versions of Naive Bayes included in Scikit-Learn, including GaussianNB, BernoulliNB, and MultinomialNB.In the advanced section of the course, you will learn about how Naive Bayes really works under the hood. You will also learn how to implement several variants of Naive Bayes from scratch, including Gaussian Naive Bayes, Bernoulli Naive Bayes, and Multinomial Naive Bayes. The advanced section will require knowledge of probability, so be prepared!Thank you for reading and I hope to see you soon!Suggested Prerequisites:Decent Python programming skillComfortable with data science libraries like Numpy and MatplotlibFor the advanced section, probability knowledge is requiredWHAT ORDER SHOULD I TAKE YOUR COURSES IN?Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including my free course)UNIQUE FEATURESEvery line of code explained in detail - email me any time if you disagreeLess than 24 hour response time on Q & A on averageNot afraid of university-level math - get important details about algorithms that other courses leave out
