|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/feature-selection-for-machine-learning/
课程评论：没有评论
**Coursera课程：《机器学习中的特征选择》内容摘要** 本课程是目前线上最全面、最深入的特征选择课程，旨在帮助学员掌握从数据集中挑选最有效变量的技巧，从而构建更简洁、更快、更可靠且更易于理解的机器学习模型。 **课程目标受众：** * 已初步接触数据科学，了解常用机器学习模型（如线性回归、决策树）并有过实践经验。 * 熟悉数据预处理技术（如处理缺失值、变量转换、类别变量编码）。 * 认识到许多数据集特征数量庞大，其中存在大量冗余、不具预测性或难以判断其价值的特征。 * 渴望了解如何在实践中找出最具预测性的特征，判断哪些特征可以保留，哪些可以舍弃。 * 希望学习专业、规范的代码实现方法。 * 对科技公司在特征选择方面的实际操作方法感到好奇。 **课程核心内容：** 本课程将教授一系列在不同组织和数据科学竞赛中广泛应用的特征选择方法，具体包括： * **去除低方差特征：** 学习识别和移除信息量较低的特征。 * **识别冗余特征：** 掌握检测和处理高度相似或重复特征的技巧。 * **基于统计检验的特征选择：** 利用统计学原理从数据中筛选出与目标变量显著相关的特征。 * **基于模型性能变化的特征选择：** 通过观察模型在不同特征子集下的表现来评估特征的重要性。 * **基于模型重要性评分的特征选择：** 利用模型自身赋予的特征重要性来指导选择。 * **专业的代码实现：** 学习如何优雅、高效且专业地编写特征选择相关的Python代码。 * **利用Python库：** 熟练运用Python、Scikit-learn、pandas和mlxtend等现有库进行特征选择。 **学习收获：** 学员将学会多种技术来选择和比较不同的特征子集，最终找出能够构建最简洁、预测能力最强的机器学习模型的最优特征组合。这将显著缩短将预测模型投入生产的时间。 **课程形式：** * 包含约70个讲座，总时长约8小时。 * 所有主题均配有实际操作的Python代码示例，可供参考、练习和复用。 * 课程内容会定期更新，以适应Python库的新版本发布和新的特征选择技术的出现。 **结论：** 立即加入本课程，掌握特征选择的强大能力，构建更精简、更高效、更可靠的机器学习模型！
Welcome to Feature Selection for Machine Learning, the most comprehensive course on feature selection available online.In this course, you will learn how to select the variables in your data set and build simpler, faster, more reliable and more interpretable machine learning models.Who is this course for?You've given your first steps into data science, you know the most commonly used machine learning models, you probably built a few linear regression or decision tree based models. You are familiar with data pre-processing techniques like removing missing data, transforming variables, encoding categorical variables. At this stage you've probably realized that many data sets contain an enormous amount of features, and some of them are identical or very similar, some of them are not predictive at all, and for some others it is harder to say.You wonder how you can go about to find the most predictive features. Which ones are OK to keep and which ones could you do without? You also wonder how to code the methods in a professional manner. Probably you did your online search and found out that there is not much around there about feature selection. So you start to wonder: how are things really done in tech companies?This course will help you! This is the most comprehensive online course in variable selection. You will learn a huge variety of feature selection procedures used worldwide in different organizations and in data science competitions, to select the most predictive features.What will you learn?I have put together a fantastic collection of feature selection techniques, based on scientific articles, data science competitions and of course my own experience as a data scientist.Specifically, you will learn:How to remove features with low varianceHow to identify redundant featuresHow to select features based on statistical testsHow to select features based on changes in model performanceHow to find predictive features based on importance attributed by modelsHow to code procedures elegantly and in a professional mannerHow to leverage the power of existing Python libraries for feature selectionThroughout the course, you are going to learn multiple techniques for each of the mentioned tasks, and you will learn to implement these techniques in an elegant, efficient, and professional manner, using Python, Scikit-learn, pandas and mlxtend.At the end of the course, you will have a variety of tools to select and compare different feature subsets and identify the ones that returns the simplest, yet most predictive machine learning model. This will allow you to minimize the time to put your predictive models into production.This comprehensive feature selection course includes about 70 lectures spanning ~8 hours of video, and ALL topics include hands-on Python code examples which you can use for reference and for practice, and re-use in your own projects.In addition, I update the course regularly, to keep up with the Python libraries new releases and include new techniques when they appear.So what are you waiting for? Enroll today, embrace the power of feature selection and build simpler, faster and more reliable machine learning models.
