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所在平台: Udemy |
课程主页: https://www.udemy.com/course/unsupervised-machine-learning-with-python/
课程评论：没有评论
**课程名称：** Python 无监督机器学习 **课程概述：** 本课程旨在帮助学员深入理解并掌握无监督机器学习的核心算法，并通过Python编程将其应用于实际数据集。课程将涵盖以下关键主题： * **聚类算法：** 层次聚类、DBSCAN、K-Means、高斯混合模型。 * **降维技术：** 主成分分析 (PCA)。 **课程特色：** * **深入理论：** 详细讲解算法背后的数学原理，包括正态分布、期望最大化 (EM) 算法、奇异值分解 (SVD) 等。 * **实践导向：** 详细讲解如何将算法转化为Python代码，并提供代码设计和向量化利用的教学。 * **丰富练习：** 提供大量的编程题和理论题，并附带解决方案，帮助学员巩固学习。 * **案例研究：** 使用真实世界的数据集（如鸢尾花数据集、MNIST数字数据集、BBC文本数据集）进行降维和聚类分析的案例研究。 * **可视化辅助：** 大量使用图表和动画来增强对课程内容的理解。 * **资源齐全：** 所有教学资源（演示文稿、补充文档、代码示例、练习题答案）均可在课程 GitHub 站点下载。 * **近期更新：** 包含 Autoencoder 的示例和演示（2021.08.28），以及代码兼容性更新（2021.11.02），并新增了英文视频字幕。 **目标受众：** 对机器学习/数据科学感兴趣的科学家、工程师、程序员以及其他相关人士。 **先修知识要求：** * **无需机器学习基础。** * **基础线性代数：** 向量、转置、矩阵、矩阵乘法、逆、行列式、线性空间。 * **基础概率与统计：** 均值、协方差矩阵、正态分布。 * **Python 3 编程：** 熟悉 Python 3 编程，并能在本地安装（如 Anaconda）运行 Python 程序（命令行和 Jupyter Notebooks）。
Course Outcome:After taking this course, students will be able to understand and implement in Python algorithms of Unsupervised Machine Learning and apply them to real-world datasets.Course Topics and Approach:Unsupervised Machine Learning involves finding patterns in datasets. The core of this course involves study of the following algorithms:Clustering: Hierarchical, DBSCAN, K Means & Gaussian Mixture ModelDimension Reduction: Principal Component AnalysisUnlike many other courses, this course:Has a detailed presentation of the the math underlying the above algorithms, including normal distributions, expectation maximization, and singular value decomposition. Has a detailed explanation of how algorithms are converted into Python code with lectures on code design and use of vectorizationHas questions (programming and theory) and solutions that allow learners to get practice with the course materialThe course codes are then used to address case studies involving real-world data to perform dimension reduction/clustering for the Iris Flowers Dataset, MNIST Digits Dataset (images), and BBC Text Dataset (articles). Course Audience:This course is designed for:Scientists, engineers, and programmers and others interested in machine learning/data scienceNo prior experience with machine learning is neededStudents should have knowledge ofBasic linear algebra (vectors, transpose, matrices, matrix multiplication, inverses, determinants, linear spaces)Basic probability and statistics (mean, covariance matrices, normal distributions)Python 3 programmingStudents should have a Python installation, such as the Anaconda platform, on their machine with the ability to run programs in the command window and in Jupyter NotebooksTeaching Style and Resources:Course includes many examples with plots and animations used to help students get a better understanding of the materialCourse has many exercises with solutions (theoretical, Jupyter Notebook, and programming) to allow students to gain additional practiceAll resources (presentations, supplementary documents, demos, codes, solutions to exercises) are downloadable from the course Github site.2021.08.28 Update: Section 9.5: added Autoencoder exampleSection 9.6: added this new section with an Autoencoder Demo 2021.11.02 Update:Sections 2.3, 2.4, 3.4, 4.3: updates so codes can run in more recent versions of python and matplotlib and updates to presentations to point out the changes2021.11.02 Update:Added English captions to the course videos
