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Dealing with Data Imbalances
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Classroom Contents
ML Foundations I - ML Basics
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- 1 The difference between AI and machine learning
- 2 Types of Machine Learning
- 3 Understanding Data
- 4 Cleaning Data
- 5 Preparing Data
- 6 Ordinal/nominal and clipping
- 7 Introduction to Supervised Learning
- 8 Decision Trees
- 9 Introduction to ML Ops
- 10 Performance Measurement for Machine Learning
- 11 Dealing with Data Imbalances
- 12 Cross-validation and bootstrapped confidence
