Overview
Discover when and how to effectively apply machine learning, with practical tips and real-world examples from open-source projects and cryptocurrency trading.
Syllabus
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- Introduction to Machine Learning
-- Overview of Machine Learning
-- Historical Context and Evolution
-- Key Concepts and Terminology
- Identifying Machine Learning Problems
-- Types of Problems Solved by Machine Learning
-- Characteristics of Successful ML Applications
-- Case Studies: Open-Source Projects
- Data Considerations
-- Data Quality and Quantity Requirements
-- Data Preprocessing Techniques
-- Feature Selection and Engineering
-- Practical Example: Data from Cryptocurrency Markets
- Choosing the Right Machine Learning Approach
-- Supervised vs Unsupervised Learning
-- Understanding Classification, Regression, Clustering, and Dimensionality Reduction
-- Algorithm Selection: Pros and Cons
-- Practical Demonstration: Selecting Models for Trading Strategies
- Implementing Machine Learning Solutions
-- Steps in Building an ML Model
-- Model Training, Validation, and Testing
-- Performance Metrics and Model Evaluation
-- Practical Frameworks and Tools
- Deployment Considerations
-- Scaling Machine Learning Solutions
-- Integrating ML in Existing Systems
-- Monitoring and Maintenance of Deployed Models
-- Deployment Case Study: Real-Time Prediction in Cryptocurrency Trading
- Common Pitfalls and Warnings
-- Overfitting and Underfitting
-- Bias and Fairness in Models
-- Data Leakage Issues
-- Ethical and Regulatory Considerations
- Tips and Best Practices
-- Iterative Development and Feedback Loops
-- Continuous Learning and Model Updating
-- Leveraging Community and Open-Source Contributions
-- Lessons Learned from Industry Failures and Successes
- Conclusion and Future Trends
-- Emerging Trends in Machine Learning
-- Speculating on the Future of ML in Trading and Open-Source
-- Final Thoughts and Course Recap
- Additional Resources
-- Recommended Readings
-- Online Communities and Forums
-- Tools and Libraries for Further Exploration
Taught by
Tags
On-Demand
