On-Chain Data for ML
Sourcing and indexing transactions, events, and traces at scale · Feature engineering: contract age, holder distribution, liquidity patterns · Building labelled datasets from known exploits and verified contracts
- 01Understand and apply: Sourcing and indexing transactions, events, and traces at scale
- 02Understand and apply: Feature engineering: contract age, holder distribution, liquidity patterns
- 03Understand and apply: Building labelled datasets from known exploits and verified contracts
Sourcing and indexing transactions, events, and traces at scale
This section covers sourcing and indexing transactions, events, and traces at scale. Content for this lesson is being developed by our practitioner team and will be available when the program launches.
Feature engineering: contract age, holder distribution, liquidity patterns
This section covers feature engineering: contract age, holder distribution, liquidity patterns. Content for this lesson is being developed by our practitioner team and will be available when the program launches.
Building labelled datasets from known exploits and verified contracts
This section covers building labelled datasets from known exploits and verified contracts. Content for this lesson is being developed by our practitioner team and will be available when the program launches.
- 01Complete the hands-on lab for on-chain data for ml.
- 02Review the provided case study and answer the reflection questions.
- ✓Sourcing and indexing transactions, events, and traces at scale
- ✓Feature engineering: contract age, holder distribution, liquidity patterns
- ✓Building labelled datasets from known exploits and verified contracts