The Foundations of Machine Learning [Updated June 2022]

本课程的目标是为您提供在计算机科学的所有领域（特别是数据科学和机器学习）取得优异成绩所需的坚实基础。问题是大多数概率和统计课程都过于理论化。

他们在没有讨论应用程序的重要性的情况下纠结于数学。应用程序始终处于次要地位。

在本课程中，我们采用面向代码的方法。我们通过代码应用所有概念。事实上，我们跳过了所有与计算机科学无关的无用理论。相反，我们专注于对数据科学、机器学习和其他计算机科学领域更有用的概念。例如，许多概率课程跳过贝叶斯推理。我们将很快了解这个极其重要的概念，并给予应有的关注，因为它被广泛认为是分析的未来！

通过这种方式，您可以在尽可能短的时间内学习该主题中最重要的概念，而无需处理不太相关主题的细节。一旦你对重要的东西有了直觉，你甚至可以自己学习最新最好的模型！

.MP4, AVC, 1920×1080, 30 fps | English, AAC, 2 Ch | 6h 35m | 7.9 GB
Instructor: Dr. Mohammad Nauman

The objective of this course is to give you a solid foundation 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 math 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. 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 will get to this immensely important concept rather quickly and give it 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!