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
Introduction to Data Science
- Defining Data Science
- The Data Science Lifecycle
- Essential Data Science Tools and Techniques
- Overview of Microsoft Azure Machine Learning
Data Preparation
- Identifying Data Sources and Types
- Cleaning and Transforming Data
- Techniques for Feature Engineering
Model Construction and Training
- Supervised Learning Approaches
- Unsupervised Learning Approaches
- Strategies for Model Selection and Assessment
- Interpreting Model Results
Model Deployment
- Deploying Solutions to Azure
- Ensuring Scalability and Optimal Performance
- Administering Deployed Models
Assessing Model Performance
- Key Metrics for Model Evaluation
- Optimising Model Performance
- Controlling Model Versions
Recap and Exam Readiness
- Reinforcement of Core Concepts
- Strategic Advice for Exam Success
- Practical Mock Examination
Requirements
- A foundational grasp of machine learning principles along with practical experience in data analytics.
- Basic proficiency in programming and data manipulation is also advised.
Target Audience
- Data scientists.
- Data analysts.
- Individuals seeking to deepen their understanding of machine learning and prepare for the DP-100 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)
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.
