Duration
7 hours (usually 1 day including breaks)
Requirements
- Basic understanding or familiarity with blockchain
- Basic understanding of company structures
Overview
Decentralized Autonomous Organizations (DAOs) are organizations that run autonomously and make decentralized growth decisions through the use of blockchain technology.
In this instructor-led, live training, participants will learn how DAOs work and decide whether using a DAO will benefit their organization.
By the end of this training, participants will be able to:
- Understand the basics of Decentralized Autonomous Organizations (DAOs)
- Know how DAOs work and operate
- Explore current and potential use cases of DAOs
- Understand the advantages and disadvantages (risk) of using a DAO
Audience
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- To request a customized training for this course, please contact us to arrange.
Course Outline
Overview of Blockchain Technology
- Blockchain
- Ethereum
- Smart Contracts
Introduction to Decentralized Autonomous Organizations (DAOs)
- Examples of DAOs
- Legal Recognition of DAOs
Understanding Safety and Security of DAOs
Structure of DAOs and How DAOs Work
- Tokens
- Voting
- Operating DAOs
Advantages and Disadvantages of DAOs
What Necessary Components Make DAOs Successful?
Understanding Whether Your Business Needs to be a DAO or Not
Summary and Conclusion
Duration
21 hours (usually 3 days including breaks)
Requirements
- Should have basic knowledge of business operation, and technical knowledge as well
- Must have basic understanding of software and systems
- Basic understanding of Statistics (in Excel levels)
Overview
This training course is for people that would like to apply Machine Learning in practical applications for their team. The training will not dive into technicalities and revolve around basic concepts and business/operational applications of the same.
Target Audience
- Investors and AI entrepreneurs
- Managers and Engineers whose company is venturing into AI space
- Business Analysts & Investors
Course Outline
Introduction to Neural Networks
Introduction to Applied Machine Learning
- Statistical learning vs. Machine learning
- Iteration and evaluation
- Bias-Variance trade-off
Machine Learning with Python
- Choice of libraries
- Add-on tools
Machine learning Concepts and Applications
Regression
- Linear regression
- Generalizations and Nonlinearity
- Use cases
Classification
- Bayesian refresher
- Naive Bayes
- Logistic regression
- K-Nearest neighbors
- Use Cases
Cross-validation and Resampling
- Cross-validation approaches
- Bootstrap
- Use Cases
Unsupervised Learning
- K-means clustering
- Examples
- Challenges of unsupervised learning and beyond K-means
Short Introduction to NLP methods
- word and sentence tokenization
- text classification
- sentiment analysis
- spelling correction
- information extraction
- parsing
- meaning extraction
- question answering
Artificial Intelligence & Deep Learning
Technical Overview
- R v/s Python
- Caffe v/s Tensor Flow
- Various Machine Learning Libraries
Industry Case Studies
