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Duration 21 hours
Course Outline
Foundations of Data Science
- Definition and Scope of Data Science
- The Data Science Lifecycle
- Tools and Methodologies in Data Science
- Overview of Microsoft Azure Machine Learning
Data Preparation Strategies
- Identifying Data Sources and Classifications
- Data Cleansing and Transformation Protocols
- Advanced Feature Engineering Techniques
Model Development and Training
- Application of Supervised Learning
- Application of Unsupervised Learning
- Model Selection Criteria and Evaluation Frameworks
- Analysis and Interpretation of Model Outputs
Model Deployment Procedures
- Deploying Solutions to Microsoft Azure
- Optimizing Scalability and System Performance
- Operational Management of Deployed Models
Performance Assessment
- Standard Metrics for Model Evaluation
- Strategies for Tuning Model Performance
- Governance of Model Versions
Summary and Certification Readiness
- Recap of Core Concepts
- Effective Strategies for Exam Preparation
- Simulated Practical Assessment
Requirements
- A foundational grasp of machine learning concepts and practical experience with data analytics tools
- Proficiency in basic programming and data manipulation is strongly advised
Intended Audience
- Professional Data Scientists
- Data Analysts
- Individuals seeking to develop machine learning competencies and prepare for the DP-100 certification examination
Testimonials (3)
Learning that the QGIS and a tool that can used by other different professionals such land survey
Bame Duncan Koko - Bentel Technologies (Pty) Ltd
Course - QGIS for Geographic Information System
How to use open satellites data for real applications
Tshering Dorji - Druk Holding and Investments
Course - Advanced Geographic Information Systems (GIS)
Equipped with examples