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Duration 21 hours
Course Outline
Introduction to Data Science
- Defining Data Science
- The Data Science Workflow
- Essential Data Science Tools and Methods
- Overview of Microsoft Azure Machine Learning
Data Preparation
- Data Sources and Classifications
- Data Cleaning and Transformation Processes
- Feature Engineering Techniques
Model Construction and Training
- Supervised Learning Approaches
- Unsupervised Learning Methods
- Selecting and Evaluating Models
- Understanding and Interpreting Model Outputs
Model Deployment
- Deploying Models on Azure
- Ensuring Scalability and Performance
- Oversight of Deployed Models
Assessing Model Performance
- Key Metrics for Model Evaluation
- Optimizing Model Performance
- Version Control and Model Management
Recap and Exam Readiness
- Review of Critical Concepts
- Strategic Tips for Exam Preparation
- Practical Hands-on Mock Exam
Requirements
- A solid grasp of machine learning principles and practical experience in data analytics.
- It is also advisable to have familiarity with programming basics and data manipulation techniques.
Target Audience
- Data scientists
- Data analysts
- Individuals seeking to master machine learning concepts and prepare for the DP-100 exam
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)
Hands-on examples allowed us to get an actual feel for how the program works. Good explanations and integration of theoretical concepts and how they relate to practical applications.
