An Analysis of Machine Learning Algorithms for Real-Time Data Processing in Cloud Computing

1. Introduction 2. Fundamentals of Machine Learning 2.1 Definition and Concepts 2.2 Types of Machine Learning Algorithms 2.3 Supervised vs Unsupervised Learning 3. Real-Time Data Processing 3.1 Definition and Importance 3.2 Challenges in Real-Time Processing 3.3 Use Cases and Applications 4. Cloud Computing Overview 4.1 Introduction to Cloud Computing 4.2 Cloud Service Models (IaaS, PaaS, SaaS) 4.3 Benefits of Cloud Computing 5. Machine Learning in Cloud Environments 5.1 Integration of ML and Cloud 5.2 Advantages and Challenges 5.3 Current Trends and Practices 6. Analysis of Selected Algorithms 6.1 Decision Trees 6.2 Neural Networks 6.3 Support Vector Machines 7. Performance Metrics and Evaluation 7.1 Criteria for Evaluation 7.2 Limitations of Current Metrics 7.3 Novel Approaches to Evaluation 8. Future Directions and Research Opportunities 8.1 Emerging Technologies in ML 8.2 Potential Improvements in Algorithms 8.3 Long-Term Impact on Cloud Computing