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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-bayesian-linear-regression-in-python/
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**课程名称：** 数据科学：Python中的贝叶斯线性回归 **课程概述：** 本课程是作者多年来贝叶斯机器学习系列课程的延续，旨在系统介绍贝叶斯线性回归。与之前侧重A/B测试的课程不同，本课程将深入探讨机器学习中的参数化模型。 线性回归是机器学习的入门模型，本课程也将采用相同的方式引导学习者进入贝叶斯机器学习的世界。贝叶斯线性回归因其易于从非贝叶斯线性回归过渡、存在闭式解等优点，是学习贝叶斯机器学习最理想的起点。 此外，课程强调了线性回归（包括贝叶斯变体）在现实世界中的实用性。通过循序渐进地接触贝叶斯思想，即使面对复杂的数学推导，学习者也能保持对实际应用的关注，避免被数学淹没。 虽然贝叶斯机器学习对数学要求较高，涉及大量的代数运算，但作者鼓励学习者坚持到底，因为最终的收获将是令人满意的，并会为贝叶斯方法优雅的特性所折服。 **适合人群：** * 已完成作者的“线性回归”和“A/B测试”课程的学习者。 **建议先修知识：** * **Python编程：** 掌握if/else语句、循环、列表、字典、集合。 * **Numpy和Pandas编程：** 熟悉矩阵和向量运算，以及CSV文件的加载。 * **基础数学：** 具备微积分、线性代数和概率论知识。 * **线性回归：** 了解线性回归的基本原理。 * **贝叶斯机器学习：** 熟悉“Python中的贝叶斯机器学习：A/B测试”课程，并了解共轭先验（conjugate priors）的概念。
Welcome to Bayesian Linear Regression!I first started this course series on Bayesian Machine Learning many years ago, with a course on A/B Testing. I had always intended to expand the series (there's a lot to cover!) but kept getting pulled in other directions.Today, I am happy to announce that the Bayesian Machine Learning series is finally back on track!In the first course, a lot of students asked, "but where is the 'machine learning'?", since they thought of machine learning from the typical supervised/unsupervised parametric model paradigm. The A/B Testing course was never meant to look at such models, but that is exactly what this course is for.If you've studied machine learning before, then you know that linear regression is the first model everyone learns about. We will approach Bayesian Machine Learning the same way.Bayesian Linear Regression has many nice properties (easy transition from non-Bayesian Linear Regression, closed-form solutions, etc.). It is best and most efficient "first step" into the world of Bayesian Machine Learning.Also, let's not forget that Linear Regression (including the Bayesian variety) is simply very practical in the real-world. Bayesian Machine Learning can get very mathematical, so it's easy to lose sight of the big picture - the real-world applications. By exposing yourself to Bayesian ideas slowly, you won't be overwhelmed by the math. You'll always keep the application in mind.It should be stated however: Bayesian Machine Learning really is very mathematical. If you're looking for a scikit-learn-like experience, Bayesian Machine Learning is definitely too high-level for you. Most of the "work" involves algebraic manipulation. At the same time, if you can tough it out to the end, you will find the results really satisfying, and you will be awed by its elegance.Sidenote: If you made it through my Linear Regression and A/B Testing courses, then you'll do just fine.Suggested Prerequisites:Python coding: if/else, loops, lists, dicts, setsNumpy and Pandas coding: matrix and vector operations, loading a CSV fileBasic math: calculus, linear algebra, probabilityLinear regressionBayesian Machine Learning: A/B Testing in Python (know about conjugate priors)
