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Course Outline
Introduction to Data Science
- What is Data Science?
- The Data Science Process
- Data Science Tools and Techniques
- Microsoft Azure Machine Learning
Preparing Data
- Data Sources and Types
- Data Cleaning and Transformation
- Feature Engineering
Building and Training Models
- Supervised Learning
- Unsupervised Learning
- Model Selection and Evaluation
- Interpreting Model Outputs
Deploying Models
- Deploying Models to Azure
- Scalability and Performance
- Managing Deployed Models
Evaluating Model Performance
- Model Evaluation Metrics
- Tuning Model Performance
- Managing Model Versions
Summary and Exam Preparation
- Review of Key Concepts
- Exam Preparation Tips and Strategies
- Hands-on Practice Exam
Requirements
- A solid grasp of machine learning concepts and experience with data analytics.
- Familiarity with basic programming and data manipulation is also recommended.
Target Audience
- Data scientists.
- Data analysts.
- Anyone interested in learning 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.
