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所在平台: Udemy |
课程主页: https://www.udemy.com/course/cluster-analysis-unsupervised-machine-learning-python/
课程评论：没有评论
课程名称：Python中的聚类分析与无监督机器学习 课程概述： 聚类分析是无监督机器学习和数据科学的重要组成部分，对数据挖掘和大数据尤为有用，因为它可以自动识别数据中的模式，而不需要标签，这与有监督机器学习不同。在真实世界中，机器人或人工智能未必总能获得最佳答案，或者可能根本没有最优的答案。我们希望这些机器能够自主探索世界，仅通过寻找模式来学习。 这门课程首先将讨论聚类，即我们将尝试自己创建标签，通过将外观相似的数据分组来实现。课程中将介绍两种聚类方法：k均值聚类和层次聚类。随后，我们将讨论高斯混合模型和核密度估计，学习如何“了解”一组数据的概率分布。有趣的是，在某些条件下，高斯混合模型与k均值聚类是完全相同的，我们将在课程中证明这一点。 所有讲到的算法都是机器学习和数据科学的基础，因此如果你希望自动发现数据中的模式，而无需人工标注数据，那么这门课程非常适合你。课程材料是免费的，你可以通过简单命令在Windows、Linux或Mac上下载和安装Python、Numpy和Scipy。 本课程专注于“如何构建和理解”，而不仅仅是“如何使用”。任何人阅读文档后都可以在15分钟内学习如何使用API。这门课不只是关于“记忆事实”，而是通过实验“亲自体验”。它将教你如何可视化模型内部发生的事情。若你希望对机器学习模型有更深层次的理解，这门课程将非常适合你。 课程提出的理念是：“如果你不能实现它，你就不能理解它。”或者引用伟大物理学家理查德·费曼的话：“我无法创造的东西我就无法理解。”我的课程是唯一可以从零开始实现机器学习算法的课程，其他课程可能只教你如何将数据输入到库中，但你真的需要用三行代码实现这件事吗？ 建议的先修知识包括：矩阵加法、乘法、概率、Python编程（if/else、循环、列表、字典、集合），以及Numpy编程（矩阵和向量运算，加载CSV文件）。有关课程的最佳学习顺序，请查看“机器学习与人工智能先决条件路线图”的讲座（可以在我任何课程的FAQ中找到，包括免费的Numpy课程）。
Cluster analysis is a staple of unsupervised machine learning and data science.It is very useful for data mining and big data because it automatically finds patterns in the data, without the need for labels, unlike supervised machine learning.In a real-world environment, you can imagine that a robot or an artificial intelligence won't always have access to the optimal answer, or maybe there isn't an optimal correct answer. You'd want that robot to be able to explore the world on its own, and learn things just by looking for patterns.Do you ever wonder how we get the data that we use in our supervised machine learning algorithms?We always seem to have a nice CSV or a table, complete with Xs and corresponding Ys.If you haven't been involved in acquiring data yourself, you might not have thought about this, but someone has to make this data!Those "Y"s have to come from somewhere, and a lot of the time that involves manual labor.Sometimes, you don't have access to this kind of information or it is infeasible or costly to acquire.But you still want to have some idea of the structure of the data. If you're doing data analytics automating pattern recognition in your data would be invaluable.This is where unsupervised machine learning comes into play.In this course we are first going to talk about clustering. This is where instead of training on labels, we try to create our own labels! We'll do this by grouping together data that looks alike.There are 2 methods of clustering we'll talk about: k-means clustering and hierarchical clustering.Next, because in machine learning we like to talk about probability distributions, we'll go into Gaussian mixture models and kernel density estimation, where we talk about how to "learn" the probability distribution of a set of data.One interesting fact is that under certain conditions, Gaussian mixture models and k-means clustering are exactly the same! We'll prove how this is the case.All the algorithms we'll talk about in this course are staples in machine learning and data science, so if you want to know how to automatically find patterns in your data with data mining and pattern extraction, without needing someone to put in manual work to label that data, then this course is for you.All the materials for this course are FREE. You can download and install Python, Numpy, and Scipy with simple commands on Windows, Linux, or Mac.This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you."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:matrix addition, multiplicationprobabilityPython coding: if/else, loops, lists, dicts, setsNumpy coding: matrix and vector operations, loading a CSV fileWHAT 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)
