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Course Outline
Introduction to Data Science
- What constitutes Data Science?
- The Data Science Lifecycle
- Data Science Tools and Methodologies
- Microsoft Azure Machine Learning
Data Preparation
- Data Sources and Categories
- Data Cleaning and Transformation
- Feature Engineering
Model Construction and Training
- Supervised Learning
- Unsupervised Learning
- Model Selection and Evaluation
- Interpreting Model Outputs
Model Deployment
- Deploying Models to Azure
- Scalability and Performance Optimisation
- Managing Deployed Models
Assessing Model Performance
- Performance Metrics
- Optimising Model Performance
- Version Control for Models
Summary and Exam Readiness
- Review of Key Concepts
- Exam Preparation Tips and Strategies
- Practical Practice Exam
Requirements
- A foundational understanding of machine learning concepts and experience in data analytics.
- Familiarity with programming basics and data manipulation is also recommended.
Audience
- Data scientists.
- Data analysts.
- Anyone interested in learning about machine learning and preparing for the DP-100 exam.
21 Hours
Testimonials (2)
Doing Exercise
Joe Pang - Lands Department, Hong Kong
Course - QGIS for Geographic Information System
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.
