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所在平台: Udemy |
课程主页: https://www.udemy.com/course/complete-bootcamp-2021-feature-selection-using-python/
课程评论：没有评论
**课程名称：2021 完整训练营：使用 Python 进行特征选择** **课程概述：** 特征选择是机器学习/人工智能流程中最关键的活动之一。本课程旨在教授学员详细的特征选择知识，帮助您识别和剔除与机器学习算法相关的低相关性或无关的特征。该课程是关于特征选择的深入在线课程之一。 **课程目标受众：** * 希望构建更快、更具可解释性的机器学习模型的**数据科学家**。 * 希望了解变量之间关系的**数据分析师**。 * 准备数据科学面试的**数据科学从业者**。 * 任何对特征选择过程感兴趣的**学习者**。 * 编写机器学习代码的**AI/ML 软件工程师**。 * 教授机器学习模型的**教师**。 **学习内容：** 本课程强调“边做边学”，包含超过 8 个端到端的特征选择小项目。每种方法都配有一个项目，以确保学员能够完全理解。课程提供的代码均可下载，方便学员自行实践，增强信心。 **核心主题：** * **特征选择基础：** 什么是特征选择？ * **特征选择方法：** * **过滤法（Filter Methods）：** * 最小方差法 * F-Score（用于回归分析数据中的相关性） * ANOVA F（用于分类分析数据） * 互信息（用于回归和分类分析数据） * 卡方检验（用于分类特征和目标变量） * 使用 scikit-learn (sklearn) 实现上述方法。 * **包裹法（Wrapper Methods）：** * 前向特征选择 * 后向特征选择 * 穷举特征选择 * 使用 sklearn 和 mlxtend 实现。 * **嵌入法（Embedded Methods）：** * 嵌入法的概念介绍。 * 使用 RandomForest 选择特征。 * 使用 Extremely Randomized Trees 选择特征。 * 基于正则化的特征选择。 **立即加入课程，掌握变量选择的知识，并将所学应用到您的项目中，以构建高效且可解释的机器学习模型！**
Feature selection is one of most important activity in machine learning/Artificial Intelligence pipeline. We select all relevant features for machine learning algorithm and discard less relevant or not relevant features. Feature selection is also known as variable selection.This course will provide learner, detailed knowledge of feature selection. It is one of most detailed online course on feature selection.Who is this course for ?Data scientist who wants to create faster and more interpretable machine learning models.Data analyst who wants to relation between two variables. Data science aspirants who are preparing for data science interview.Any One who wants to learn about feature selection process.AI/ML software engineer who write code for machine learning. Teachers who are teaching Machine Learning Models. What will you learn ?In this course, you are going to learn feature selection by doing. I have included more than 8 end to end small projects on feature selection methods. Each method has one project so that learner can understand the process fully. Code provided in throughout course is downloadable. You can download code and data and run by yourself to get confidence. Knowledge gain though this course is precious and can be used in We are going to learn following topics.What is feature selection?Different methods of feature selection.Filter methods Minimum variance method F-Score using correlation for regression analysis data.Anova F for classification analysis data Mutual Information for regression and Classification analysis data.Chi-Square Scores for categorical features and TargetAll these methods implementation using sklearnWrapper Method Forward selection of features.Backward selection of features.Exhaustive feature selection.Implementation of each using sklearn and mlxtend.Embedded Method Introduction to Embedded Method for feature selection.Using RandomForest Using Extremely randomized trees to select featuresRegularization based feature selectionSo what are you waiting for? Join the course and get the knowledge of variable selection and apply it in your projects to get efficient and interpretable machine learning models.
