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Course Outline
Overview of Data Science Principles
- Defining Data Science
- The Data Science Lifecycle
- Analytical Tools and Methodologies
- Microsoft Azure Machine Learning Platform
Data Preparation Procedures
- Data Origins and Classifications
- Data Sanitization and Transformation
- Feature Construction Strategies
Model Development and Training
- Supervised Learning Approaches
- Unsupervised Learning Approaches
- Algorithm Selection and Validation
- Analysis of Model Outputs
Model Deployment Operations
- Deployment to Azure Environments for government applications where appropriate
- System Scalability and Efficiency
- Lifecycle Management of Deployed Models
Performance Assessment and Optimization
- Evaluation Criteria and Metrics
- Model Tuning and Optimization
- Version Control and Management
Concept Review and Certification Readiness
- Synopsis of Core Concepts
- Examination Strategies and Best Practices
- Simulated Practice Assessment
Requirements
- Demonstrates foundational knowledge of machine learning principles alongside practical experience in data analytics
- Proficiency in basic programming and data manipulation techniques is also advisable
Intended Audience
- Data scientists
- Data analysts
- Professionals seeking to acquire machine learning competencies and prepare for the DP-100 certification exam
21 Hours
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Equipped with examples