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.
Feature Engineering Step by Step: ML Data Preparation
缺失数据、缩放、特征提取、选择、高级技术和自动化特征工程
All Levels • 22 Lectures • Full Lifetime Access
Verified Master Instructor
TRAILER
Meet your instructor: Dr. Amar Massoud
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Active StudentsEnglish (US)
99 Languages Subtitles1h 37m
Total ContentWhat you will learn
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。
Course Content
10 sections • 22 lectures • 01:37:25 total lengthHandling 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.
Weekly Study Plan
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Requirements
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。
Target Audience
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.
About this course
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！
Career Opportunities & Job Roles
Build practical, job-ready skills and qualify for high-impact roles worldwide
Job-Ready Curriculum
Target Roles You Can Apply For
AI Solutions Engineer
Data Science Consultant
Machine Learning Practitioner
AI Workflow & Automation Specialist
Estimated Annual Salary
$105,000 – $165,000 / yr
Global Industry Demand
Fastest Growing Worldwide (#1)
In-Demand Skills You Master* Estimates based on global tech hiring benchmarks and industry compensation reports.
Model Fine-Tuning & Evaluation
Data Pipelines & Vector Analysis
Applied Generative AI Architecture
Predictive Analytics & Deployment
Meet your instructor
Dr. Amar Massoud
Main Instructor
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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