Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 21 hours
Course Outline
Foundations of Data Science
- Defining Data Science.
- Understanding the Data Science Workflow.
- Essential Tools and Methodologies.
- Overview of Microsoft Azure Machine Learning.
Data Preparation
- Identifying Data Sources and Formats.
- Cleaning and Transforming Data.
- Applying Feature Engineering Techniques.
Model Development and Training
- Supervised Learning Approaches.
- Unsupervised Learning Methods.
- Strategies for Model Selection and Assessment.
- Analyzing and Interpreting Model Results.
Model Deployment
- Publishing Models to Azure.
- Ensuring Scalability and Optimizing Performance.
- Overseeing Live Deployments.
Performance Analysis
- Key Metrics for Evaluating Models.
- Techniques for Tuning and Improving Performance.
- Version Control for Models.
Recap and Exam Readiness
- Consolidation of Core Concepts.
- Strategies and Tips for Exam Success.
- Simulated Practice Examination.
Requirements
- Solid foundational knowledge of machine learning principles and prior experience with data analytics.
- Basic proficiency in programming and data manipulation techniques is highly recommended.
Intended Audience
- Data scientists.
- Data analysts.
- Professionals aiming to master machine learning concepts and prepare for the DP-100 certification.
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.
