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逐步特征工程:机器学习数据准备

缺失数据、缩放、特征提取、选择、高级技术和自动化特征工程

Barcha darajalar • 22 Ma'ruzalar • Umrbod kirish
Dr. Amar Massoud

Tasdiqlangan usta o'qituvchi

逐步特征工程:机器学习数据准备
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O'qituvchi bilan tanishing: Dr. Amar Massoud

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1 soat 37 daq
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Siz nimani o'rganasiz

理解并应用特征工程技术以提高模型准确性。
识别并缓解偏见,确保公平和道德的特征选择。
使用FeatureTools等库实现自动化特征工程。
跟踪和记录特征版本以实现可重复性和协作。

Kurs mazmuni

10 bo'limlar • 22 ma'ruzalar • 01:37:25 umumiy uzunligi

Introduction

06:05

Master feature engineering for machine learning with a practical, hands-on approach, applying techniques from basic feature creation to PCA, imputation, encoding, regularization, and cross-validation using Python code.

Our Use Case - Housing Price Prediction Dataset

03:01

Apply feature engineering to a housing price dataset by median imputation for missing values, one-hot encoding neighborhoods, creating house age, log-transforming square footage, correlation analysis, and standardizing data.

Types of Features and Domain Knowledge

03:24

Identify and transform feature types—categorical, numerical, temporal, and text—to improve machine learning models. Leverage domain knowledge to guide feature creation, selection, and interaction terms for better performance and interpretability.

Handling Missing Data

09:03

Learn strategies to handle missing data, from mean, median, and mode imputation to modeling-based approaches like KNN and regression. Apply these to numerical and categorical features, including frequent category imputation.

Feature Transformation

01:57

Explore feature transformation techniques for machine learning, including scaling, normalization, log and power transformations, binning, and one hot encoding for categorical data.

Scaling and Normalization

05:40

Learn to scale and normalize features using standardization to zero mean and unit variance or min-max normalization to 0–1, improving model training on housing data.

Log Transformation, Power Transformation

05:16

Binning Numerical Variables

03:36

One-Hot Encoding vs. Label Encoding

04:07

Compare one-hot encoding and label encoding for transforming categorical data into numerical features, weighing dimensionality against potential ordinal assumptions in different datasets.

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Talablar

基本了解Python和机器学习概念。

熟悉Pandas和Scikit-learn。

具备基本的数据集工作经验是推荐的。

不需要高级特征工程经验。

Maqsadli auditoriya

Data science enthusiasts looking to deepen their skills in feature engineering.

Beginner and intermediate data scientists aiming to improve model performance.

Machine learning practitioners who want practical, hands-on experience.

Developers interested in ethical AI and responsible data practices.

Ushbu kurs haqida

通过我们全面的特征工程课程,充分释放机器学习模型的潜力。该课程为数据科学爱好者、机器学习从业者和开发人员设计,涵盖了基本和高级特征工程技术,这些技术将提升模型的性能、准确性和可解释性。

从处理缺失数据和转换特征到使用FeatureTools等库进行自动化特征工程,您将学习创建强大、相关特征的技能。了解缩放、归一化、独热编码和特征提取等关键技术。了解何时应用多项式和交互特征以揭示更深层次的模式,并利用时间序列数据的时间特征。本课程还介绍了关键的伦理考虑,向您展示如何避免偏见、确保公平性并增强特征的可解释性。

通过实践示例、一致的真实世界用例和每种方法的Python代码,您将获得可以立即应用的实践经验。您还将学习文档和版本控制的最佳实践,确保您的特征组织良好且可重复。最后,通过持续学习和迭代技术,您将获得保持模型相关性和有效性的工具,以随着数据的发展而保持相关性。

无论您是想完善特征工程技能还是自动化工作流程,本课程都提供了构建高性能、符合道德的模型所需的知识和工具。立即注册,迈向掌握机器学习中的特征工程!

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O'qituvchingiz bilan tanishing
Dr. Amar Massoud
5.0 Baho
5 Talabalar
Profili Go'runtule
Dr. Amar Massoud
Bosh murabbiy
PhD in computer science and IT manager with 36 years technical experience in various fields including IT Security, IT Governance, IT Service Management , Software Development, Project Management, Business Analysis and Software Architecture. I hold 80+ IT certifications such as : ITIL 4 Master, ITIL 3 Expert ISO 27001 Auditor, ComptIA Security+, GSEC, CEH, ECSA, CISM, CISSP, CISA PGMP, MSP PMP, PMI-ACP, Prince2 Practitioner, Praxis, Scrum Master COBIT 2019 Implementor, COBIT 5 Assessor/Implementer TOGAF certified Lean Specialist, VSM Specialist PMI RMP, ISO 31000 Risk Manager, ISO 22301 Lead Auditor PMI-PBA, CBAP Lean Six Sigma Black Belt, ISO 9001 Implementer Azure Administrator, Azure DevOps Expert, AWS Practitioner And many more....

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