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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-data-science-masterclass/
课程评论：没有评论
Coursera《Python与R机器学习与数据科学大师班》课程内容总结： 该课程旨在成为入门机器学习最简单直接的方式，通过200多节课、测验、实践案例，逐步教授机器学习知识。 **核心亮点：** * **深入浅出的讲解：** 每个主题都先讲解概念直觉，再辅以Python和R两种语言的代码实现。 * **丰富的实践案例：** 通过“估算二手车价值”、“编写垃圾邮件过滤器”、“诊断乳腺癌”等真实数据分析，巩固学习。 * **双语言支持：** 所有代码示例均提供Python和R版本，学习者可选择任一或两种语言学习。 * **实战能力培养：** 课程结束后，学员能将机器学习应用于自有数据，做出明智决策。包括： * 理解何时选用何种模型并进行比较。 * 分析数据特征的重要性，判断是否需要额外数据以及如何进行数据预处理。 * **覆盖关键主题：** * **回归：** 线性回归、多项式回归。 * **分类：** 逻辑回归、朴素贝叶斯、决策树、随机森林。 * **掌握机器学习工作流程：** * 数据读取与模型准备。 * 超参数调优（找到模型的最佳参数）。 * 模型比较：理解准确率的局限性，掌握K折交叉验证、判定系数等评估方法。 * **使用主流工具：** 课程使用Sklearn、NLTK、caret、data.table等业界常用工具。 * **直观的数学解释：** 侧重生动形象的图形化解释，而非枯燥的数学公式。 **学习目标：** 为学员提供进入机器学习世界的理想化入门体验。
This course contains over 200 lessons, quizzes, practical examples,.- the easiest way if you want to learn Machine Learning. Step by step I teach you machine learning. In each section you will learn a new topic - first the idea / intuition behind it, and then the code in both Python and R.Machine Learning is only really fun when you evaluate real data. That's why you analyze a lot of practical examples in this course:Estimate the value of used carsWrite a spam filterDiagnose breast cancerAll code examples are shown in both programming languages - so you can choose whether you want to see the course in Python, R, or in both languages!After the course you can apply Machine Learning to your own data and make informed decisions:You know when which models might come into question and how to compare them. You can analyze which columns are needed, whether additional data is needed, and know which data needs to be prepared in advance. This course covers the important topics:RegressionClassificationOn all these topics you will learn about different algorithms. The ideas behind them are simply explained - not dry mathematical formulas, but vivid graphical explanations.We use common tools (Sklearn, NLTK, caret, data.table,...), which are also used for real machine learning projects. What do you learn?Regression:Linear RegressionPolynomial RegressionClassification:Logistic RegressionNaive BayesDecision treesRandom ForestYou will also learn how to use Machine Learning:Read in data and prepare it for your modelWith complete practical example, explained step by stepFind the best hyper parameters for your model"Parameter Tuning"Compare models with each other:How the accuracy value of a model can mislead you and what you can do about itK-Fold Cross ValidationCoefficient of determinationMy goal with this course is to offer you the ideal entry into the world of machine learning.
