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Computer Science
Artificial Intelligence
Machine Learning
Machine Learning with Scikit-Learn
1. Introduction to Scikit-Learn
2. Core Scikit-Learn Concepts and API
3. Machine Learning Fundamentals
4. Data Preprocessing and Feature Engineering
5. Supervised Learning: Regression
6. Supervised Learning: Classification
7. Model Evaluation and Metrics
8. Improving Model Performance
9. Unsupervised Learning
10. Building Machine Learning Pipelines
11. Working with Text Data
12. Advanced Topics
13. Model Persistence and Deployment
14. Performance Optimization
15. Best Practices and Common Pitfalls
Performance Optimization
Computational Efficiency
Algorithm Complexity
Memory Usage
Parallel Processing
Scikit-learn Performance Tips
Sparse Matrix Usage
Efficient Data Types
Warm Start Options
Hardware Considerations
CPU Optimization
Memory Management
GPU Acceleration Limitations
Profiling and Benchmarking
Performance Measurement
Bottleneck Identification
Optimization Strategies
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15. Best Practices and Common Pitfalls