When to Use Machine Learning - Tips, Tricks and Warnings

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

• 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

---

Subjects

Conference Talks