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所在平台: Udemy |
课程主页: https://www.udemy.com/course/the-fundamentals-of-machine-learning/
课程评论：没有评论
课程名称：机器学习基础 课程概述：本课程是机器学习的入门课程，将涵盖广泛的话题，旨在逐步教会学员从处理数据集到模型交付的全过程。课程不要求学生具备先前的知识，但具备一定的Python编程基础和基本微积分知识将更有助于学习。课程的设计目的是提供与正规本科计算机科学项目中机器学习或人工智能入门课程相同的知识和培训。该课程与著名的《统计学习导论》相当，这本教材是由顶尖学者Trevor Hastie和Rob Tibshirani编写的。课程借鉴了斯坦福大学的《统计学习导论》，由Yiqiao Yin讲授，课程材料由一支超过5年行业经验的优秀讲师团队提供，所有讲师均来自常春藤盟校，热衷于分享他们在行业中的经验。 课程内容包括： - 机器学习简介 - 统计学习基础 - 线性回归 - 分类 - 抽样与自助法 - 模型选择与正则化 - 超越线性 - 基于树的方法 - 支持向量机 - 深度学习 - 无监督学习 - 分类指标 此课程分为三个部分：讲座系列。
This is an introduction course of machine learning. The course will cover a wide range of topics to teach you step by step from handling a dataset to model delivery. The course assumes no prior knowledge of the students. However, some prior training in python programming and some basic calculus knowledge is definitely helpful for the course. The expectation is to provide you the same knowledge and training as that is provided in an intro Machine Learning or Artificial Intelligence course at a credited undergraduate university computer science program. The course is comparable to the Introduction of Statistical Learning, which is the intro course to machine learning written by none other than the greatest of all: Trevor Hastie and Rob Tibshirani! The course was modeled from the "Introduction to Statistical Learning" from Stanford University.The course is taught by Yiqiao Yin, and the course materials are provided by a team of amazing instructors with 5+ years of industry experience. All instructors come from Ivy League background and everyone is eager to share with you what they know about the industry. The course has the following topics:IntroductionBasics in Statistical LearningLinear RegressionClasificationSampling and BootstrapModel Selection & RegularizationGoing Beyond LinearityTree-based MethodSupport Vector MachineDeep LearningUnsupervised LearningClassification MetricsThe course is composed of 3 sections:Lecture series
