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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-with-python-g/
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**课程名称：** Using Python for Machine Learning **课程概述：** 本课程将带您一步步走进机器学习的世界。通过每一个教程，您将掌握新技能，加深对这个具有挑战性且收益丰厚的 数据科学子领域 的理解。 **课程内容涵盖：** * **机器学习基础：** * 什么是机器学习？ * 机器学习的特点 * 普通程序与机器学习程序的区别 * 机器学习的应用 * 机器学习的类型 * **监督学习：** * 什么是监督学习？ * 什么是强化学习？ * K近邻算法 * K近邻分类 * K近邻回归 * 详细的监督学习 * 监督学习算法 * 线性回归（含演示用例） * 模型拟合 * 逻辑回归的必要性 * 什么是逻辑回归？ * Ridge和Lasso回归 * 支持向量机 * **无监督学习：** * 什么是无监督学习？ * 什么是聚类？ * 聚类的类型 * **基于树的模型：** * 什么是决策树？ * 什么是随机森林？ * AdaBoost * 梯度提升 * 随机梯度提升 * **其他算法与技术：** * 朴素贝叶斯 * 使用天气数据集计算朴素贝叶斯 * 使用天气数据集计算熵 * 树的熵与基尼系数 * 数学介绍 * **机器学习数据预处理与管道：** * ML Pipeline * 使用SimpleImputer和SVC的Pipeline * 使用特征选择和SVC的Pipeline * 删除缺失数据 * 使用Ridge算法处理分类特征的回归 * 分类特征处理 (第一部分) * 分类特征处理 (第二部分) * 删除异常值 * 处理异常值
We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.What is Machine learningFeatures of Machine LearningDifference between regular program and machine learning programApplications of Machine LearningTypes of Machine LearningWhat is Supervised LearningWhat is Reinforcement LearningWhat is Neighbours algorithmK Nearest Neighbours classificationK Nearest Neighbours RegressionDetailed Supervised LearningSupervised Learning AlgorithmsLinear RegressionUse Case(with Demo)Model FittingNeed for Logistic RegressionWhat is Logistic Regression?Ridge and lasso regressionSupport vector MachinesPre process of Machine learning dataML PipelineWhat is Unsupervised LearningWhat is ClusteringTypes of ClusteringTree Based ModelesWhat is Decision TreeWhat is Random ForestWhat is AdaboostWhat is Gradient boostingstochastic gradient boostinngWhat is Naïve Bayes Calculation using weather datasetEntropy Calculation using weather dataset Trees Entropy and Gini Maths IntroductionPipeline with SimpleImputer and SVCPipeline with feature selection and SVCDropping Missing DataRegression with categorical features using ridge algorithmprocessing Categorical Features part2processing Categorical Featuresprocessing of machine learning data Delete Outliersprocessing of machine learning data Outliers
