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Course Outline
Foundations of Data Science
- Defining Data Science
- The Data Science Workflow
- Essential Tools and Techniques in Data Science
- Overview of Microsoft Azure Machine Learning
Data Preparation
- Understanding Data Sources and Types
- Techniques for Data Cleaning and Transformation
- Strategies for Feature Engineering
Model Construction and Training
- Supervised Learning Approaches
- Unsupervised Learning Methods
- Selecting and Evaluating Models
- Interpreting Model Outputs and Results
Model Deployment
- Deploying Solutions to Azure
- Ensuring Scalability and Optimal Performance
- Managing Deployed Models Effectively
Assessing Model Efficacy
- Understanding Key Evaluation Metrics
- Optimizing Model Performance through Tuning
- Controlling Model Versioning
Conclusion and Exam Readiness
- Recap of Core Concepts
- Effective Strategies and Tips for Exam Preparation
- Simulated Practice Examination
Requirements
- A solid grasp of machine learning fundamentals and prior experience in data analytics.
- Familiarity with basic programming principles and data manipulation techniques is also advised.
Target Audience
- Data scientists.
- Data analysts.
- Professionals seeking to enhance their machine learning knowledge and prepare for the DP-100 exam.
21 Hours
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.
