This course teaches you how to transform real-world datasets into reliable analytical assets through practical, reproducible data-cleaning techniques. You’ll learn how to evaluate categorical features and select optimal encoding strategies, measure and document data quality, and apply effective approaches to handle missing values. Using Python and pandas, you'll practice assessing cardinality, implementing target encoding, validating completeness with Great Expectations, and building transparent transformation lineage. You’ll also clean messy fields such as ages, salary outliers, and dates to ensure consistent model-ready outputs. Designed for analysts, data engineers, and ML practitioners, this course equips you with the job-ready skills needed to prepare high-quality datasets that support trustworthy insights and predictive modeling.
Data Engineering & Pipeline Reliability for Machine Learning
Data Engineering & Pipeline Reliability for Machine Learning
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您将学到什么
Transform and validate data for machine learning using encoding, cleansing, and data quality techniques
Design and orchestrate ML data pipelines that ensure reliability, freshness, and pipeline performance
Manage reproducible ML development using version control and environment management tools
您将获得的技能
- Data Transformation
- Data Validation
- Data Integrity
- Data Quality
- Dataflow
- Package and Software Management
- MLOps (Machine Learning Operations)
- Feature Engineering
- Data Preprocessing
- Cost Management
- Resource Utilization
- Extract, Transform, Load
- Virtual Environment
- Quality Assurance
- Data Cleansing
- Data Pipelines
- Version Control
- Exploratory Data Analysis
要了解的详细信息
了解顶级公司的员工如何掌握热门技能
积累特定领域的专业知识
本课程是 Machine Learning Made Easy for Software Engineers 专项课程 专项课程的一部分
在注册此课程时，您还会同时注册此专项课程。
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该课程共有10个模块
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