Google Cloud Professional Machine Learning Engineer Cert Prep: 3 Designing Data Preparation and Processing Systems
1h 1mAdvanced2023-06-09
Authors
Noah Gift
MLOps Expert | Solopreneur | Author | Adjunct Professor | CTO
Course details
Earning a Google Professional Machine Learning Engineer certification demonstrates your ability to design, build, and productionize machine learning models to solve business challenges using Google Cloud technologies, and knowledge of proven ML models and techniques.
In this third course in the certification prep series, instructor Noah Gift covers designing data preparation and processing systems. He covers topics you need to know relating to exploratory data analysis (EDA), then shows how to build data pipelines and create input features.
In this third course in the certification prep series, instructor Noah Gift covers designing data preparation and processing systems. He covers topics you need to know relating to exploratory data analysis (EDA), then shows how to build data pipelines and create input features.
Skills covered
Google CloudMachine LearningBusiness IntelligenceData EngineeringGoogleSoftware Development ToolsData AnalysisCloud PlatformsArtificial Intelligence (AI)Cert PrepCloud ComputingData ScienceBusiness Analysis and StrategyBusiness Software and ToolsSoftware Development
Concepts
Introduction
- Overview
- Course three key terminology
- Onboard to GCP
Exploring Data
- What is Google Colab
- Exploratory data analysis for life expectancy
- Data science setup with virtualenv and pip on Windows
- Graphing data for exploratory data analysis
Building Data Pipelines
- Labeling data
- Mechanical Turk labeling
- Cleaning up data
- Scaling data
- BigQuery data pipelines with Colab
Creating Input Features
- Feature engineering concepts
- Extracting features from public datasets
- Exploratory data analysis with Google BigQuery
Conclusion
- Next steps