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Course:
Outline
Introduction to Machine Learning and Data Science
- Describing Machine Learning, Data Science and AI
- The 5 different kinds of problems Machine Learning can solve
- Technology capabilities vs hype and mirrors
- Machine Learning classifications
Data Requirements for Machine Learning
- All about data selection and quality
- Understanding overfitting
- Avoiding bias in data selection
- Transparency solution development
The unagile Machine Learning Solution Process
- CRISP Method
- Understanding the data cleansing process
- Training sets and what they are for
- How the computer learns a from data
- Walk through a machine learning solution step-by-step
Interpreting Results
- Correlation and Causation
- Different result visualizations and their meaning
- Understanding Confusion Matrix
Implementation Options
- Implementation Options
- Understanding Data drift
- Ongoing Monitoring of results
- Planned obsolesce of the solution
Development Tools
- A review of common languages and libraries for creating machine learning
- No code option of development
- Incorporating Cognitive Services
- Machine Learning insights in Power BI