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所在平台: Udemy |
课程主页: https://www.udemy.com/course/applied-probability-stats-for-computer-science-ds-and-ml/
课程评论：没有评论
课程名称：概率与统计：机器学习的基础 课程概述： 在当今社会，人人都希望在机器学习和数据科学领域取得优异成绩，这并非偶然。数据被誉为新的“石油”，每个人都应能够有效地利用它。然而，要在这一领域出类拔萃并不容易，因为最新和最先进的模型似乎过于复杂。其实，只要对概率和统计有深入了解，这些模型的应用就会变得简单得多。此外，概率在计算机科学的几乎所有领域（如模拟、计算机视觉、游戏开发和人工智能等）中都非常有用。建立扎实的基础能够为你的职业发展开辟多条道路！本课程的目标就是为你提供在计算机科学各个领域（尤其是数据科学和机器学习）中成功所需的坚实基础。然而，大多数概率和统计课程过于理论化，往往陷入数学细节，而忽视了应用的重要性。在本课程中，我们采取以代码为导向的方法，通过代码应用所有概念。我们将跳过那些与计算机科学无关（但对纯科学有用）的无用理论，专注于对数据科学、机器学习及其他计算机科学领域更有用的概念。比如，许多概率课程常常忽视贝叶斯推断，而我们会迅速进入这一重要概念并给予应有的重视，因为它被广泛认为是未来分析的方向！通过这种方式，你可以在最短时间内掌握这一领域的核心概念，而无需关注不太相关主题的细节。一旦你对这些重要内容有了直觉，就能够独立学习最新的最先进的模型！请查看本课程的宣传视频（以及下面的课程内容列表），了解你将学习的主题和互动学习的风格。请记住：支付课程费用的原因在于获得支持。我会在当天内回复问题。你可以查看我的课程评价，证明这一点。因此，务必提出你遇到的任何问题或困扰。我希望所有学生都能顺利完成这门课程。让我们一起努力完成它。
Everyone wants to excel at machine learning and data science these days - and for good reason. Data is the new oil and everyone should be able to work with it. However, it's very difficult to become great in the field because the latest and greatest models seem too complicated. "Seem complicated" - but they are not! If you have a thorough understanding of probability and statistics, they would be much, much easier to work with! And that's not all - probability is useful in almost all areas of computer science (simulation, vision, game development, AI are only a few of these). If you have a strong foundation in this subject, it opens up several doors for you in your career! That is the objective of this course: to give you the strong foundations needed to excel in all areas of computer science - specifically data science and machine learning. The issue is that most of the probability and statistics courses are too theory-oriented. They get tangled in the maths without discussing the importance of applications. Applications are always given secondary importance. In this course, we take a code-oriented approach. We apply all concepts through code. In fact, we skip over all the useless theory that isn't relevant to computer science (and is useful for those pursuing pure sciences). Instead, we focus on the concepts that are more useful for data science, machine learning, and other areas of computer science. For instance, many probability courses skip over Bayesian inference. We get to this immensely important concept rather quickly and give it the due attention as it is widely thought of as the future of analysis! This way, you get to learn the most important concepts in this subject in the shortest amount of time possible without having to deal with the details of the less relevant topics. Once you have developed an intuition of the important stuff, you can then learn the latest and greatest models even on your own! Take a look at the promo for this course (and contents list below) for the topics you will learn as well as the preview lectures to get an idea of the interactive style of learning. Remember: The reason you pay for this course is support. I reply within the day. See any of my course reviews for proof of that. So make sure you post any questions you have or any problems you face. I want all my students to finish this course. Let's get through this together.
