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Classroom Contents
Not a Magic - What to Expect from Machine Learning Projects
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- 1 Intro
- 2 Machine Learning Project
- 3 Data mining
- 4 Data protection
- 5 Data cleaning
- 6 Data domain
- 7 The nature of Data
- 8 Data – sharing the progress
- 9 Data reporting
- 10 Learning data
- 11 Learning algorithms
- 12 Training: Loss and Cost funtion
- 13 Training: Objective function and loss
- 14 Training: summary
- 15 Feature engineering
- 16 Evaluate Metric: Supervised learning
- 17 Evaluate model: high variance
- 18 Evaluate: compare with what
- 19 Evaluate model: same distirbution
- 20 Deep learning
- 21 Data Pipeline - reality
- 22 Monitoring
- 23 Reports
- 24 Model deployment
- 25 A/B testing
- 26 Proof of Concept (PC)
- 27 Modelling
