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所在平台: Udemy |
课程主页: https://www.udemy.com/course/bayesian-machine-learning-in-python-ab-testing/
课程评论：没有评论
课程名称：Python中的贝叶斯机器学习：A/B 测试 课程概述：本课程专注于A/B 测试，这是一种广泛应用于营销、零售、新闻推送和在线广告等领域的比较方法。作为数据科学家，您不能仅凭直觉宣称“logo A 比 logo B 更好”，而必须使用数据和统计进行证实。传统的A/B 测试历史悠久，但充满了近似值和混淆定义。在本课程中，我们将首先进行传统的A/B 测试，以理解其复杂性，最终转向贝叶斯机器学习方法。 我们将探索如何通过自适应方法改进传统A/B 测试，这有助于解决探索与开发的困境。您将学习到ε-贪婪算法，这一算法在强化学习中相当常见，并进一步提高其性能，通过与之类似的UCB1算法。最后，我们将使用完全贝叶斯的方法来提升这两种算法的效果。贝叶斯方法为我们在机器学习中提供了全新的思维方式和概率观念。 本课程不仅限于A/B 测试，而是利用A/B 测试作为贝叶斯技术应用的具体示例。您将通过A/B 测试学习贝叶斯方法的基本工具，进而将这些贝叶斯技术应用到更高级的机器学习模型中。 课程强调实践，倡导“如果不能实现，你就不理解”的理念，引用著名物理学家理查德·费曼的一句话：“我无法创造的东西，我就不理解”。本课程将教您如何从零开始实现机器学习算法，而不是仅仅将数据插入库中。 建议的先修课程：概率（联合、边际、条件分布，连续与离散随机变量，PDF，PMF，CDF）、Python 编程（if/else 语句、循环、列表、字典、集合）、Numpy、Scipy、Matplotlib。 课程独特之处在于每行代码都有详细解释，您可以随时联系我提出不同意见，而课程中不会浪费时间在无意义的“输入”上。此外，我会介绍大学级别的数学知识，讲解其他课程常常忽略的重要算法细节。 期待在课程中见到您！
This course is all about A/B testing.A/B testing is used everywhere. Marketing, retail, newsfeeds, online advertising, and more.A/B testing is all about comparing things.If you're a data scientist, and you want to tell the rest of the company, "logo A is better than logo B", well you can't just say that without proving it using numbers and statistics.Traditional A/B testing has been around for a long time, and it's full of approximations and confusing definitions.In this course, while we will do traditional A/B testing in order to appreciate its complexity, what we will eventually get to is the Bayesian machine learning way of doing things.First, we'll see if we can improve on traditional A/B testing with adaptive methods. These all help you solve the explore-exploit dilemma.You'll learn about the epsilon-greedy algorithm, which you may have heard about in the context of reinforcement learning.We'll improve upon the epsilon-greedy algorithm with a similar algorithm called UCB1.Finally, we'll improve on both of those by using a fully Bayesian approach.Why is the Bayesian method interesting to us in machine learning?It's an entirely different way of thinking about probability.It's a paradigm shift.You'll probably need to come back to this course several times before it fully sinks in.It's also powerful, and many machine learning experts often make statements about how they "subscribe to the Bayesian school of thought".In sum - it's going to give us a lot of powerful new tools that we can use in machine learning.The things you'll learn in this course are not only applicable to A/B testing, but rather, we're using A/B testing as a concrete example of how Bayesian techniques can be applied.You'll learn these fundamental tools of the Bayesian method - through the example of A/B testing - and then you'll be able to carry those Bayesian techniques to more advanced machine learning models in the future.See you in class!"If you can't implement it, you don't understand it"Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratchOther courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...Suggested Prerequisites:Probability (joint, marginal, conditional distributions, continuous and discrete random variables, PDF, PMF, CDF)Python coding: if/else, loops, lists, dicts, setsNumpy, Scipy, MatplotlibWHAT 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 the free Numpy course)UNIQUE FEATURESEvery line of code explained in detail - email me any time if you disagreeNo wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratchNot afraid of university-level math - get important details about algorithms that other courses leave out
