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所在平台: Udemy |
课程主页: https://www.udemy.com/course/introduction-to-machine-learning-using-python/
课程评论：没有评论
Coursera“机器学习导论”课程总结 **课程概述：** 本课程是一门专注于监督学习的入门课程，旨在帮助学生理解并使用 Python 实现回归、二元分类和多分类等机器学习算法，并应用于真实世界的数据集。 **核心内容与方法：** 课程将深入讲解线性回归、逻辑回归以及用于回归和分类的神经网络。不同于其他课程，本课程将详细介绍算法背后的数学原理，包括优化算法和反向传播公式。同时，课程还会深入讲解如何将算法转化为 Python 代码，包括代码设计和向量化使用。 **实践环节：** 课程包含大量的编程和理论习题，并提供解答，以供学生巩固所学知识。习题将应用于真实世界案例研究，如图像分类、文本消息的垃圾邮件/非垃圾邮件分类以及房价预测。 **目标受众：** 适合对机器学习/数据科学感兴趣的科学家、工程师、程序员及其他人士。 **先修要求：** * 无需机器学习经验 * 具备基础线性代数（向量、矩阵乘法、转置）知识 * 具备多变量微积分（用于理解优化和反向传播公式）知识 * 掌握 Python 3 编程，并能在命令行和 Jupyter Notebooks 中运行程序（建议安装 Anaconda） **教学风格与资源：** 课程包含大量带图解的示例，以加深学生理解。提供 50 多个练习题（理论、Jupyter Notebook 和编程）及解决方案。所有课程资源（演示文稿、补充文档、演示、代码、练习题解答）均可从课程的 Github 网站下载。
Course Outcome:After taking this course, students will be able to understand and implement machine learning algorithms using Python for regression, binary classification, and multi-class classification with applications to real-world datasets.Course Topics and Approach:This introductory course on machine learning focuses on Supervised Learning, which involves finding functions that fit data and then using the functions to make predictions. Applications include image classification, text sentiment classification, house price prediction. The core of this course involves study of the following algorithms:Linear Regression, Logistic Regression, Neural Networks for regression, binary, and multiclass classificationUnlike many other courses, this course:Has a detailed presentation of the the math underlying the above algorithms including optimization algorithms and back propagation formulasHas a detailed explanation of how algorithms are converted into Python code with lectures on code design and use of vectorizationHas homework 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 including image classification, text message spam/no spam classification, and house price prediction. 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, matrix multiplication, transpose)Multivariable calculus (to follow details of optimization and backpropagation formulas)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 used to help students get a better understanding of the materialCourse has 50+ 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.
