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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 fundamental understanding of machine learning concepts and experience working with data analytics
- Familiarity with the basics of programming and data manipulation is also recommended
Audience
- Data scientists
- Data analysts
- Anyone who wants to learn about machine learning and prepare for the DP-100 exam
21 Hours
Testimonials (5)
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The trainer adapted the materials and contents to what he thought would be best for us and he succeeded. The quality of the training was excellent.
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It covered the areas i said i was interested in before the course: data relationships, using python script. Connecting to databases will be covered in the advanced module.
