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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
13.
Model Persistence and Deployment
13.1.
Model Serialization
13.1.1.
Importance of Model Persistence
13.1.2.
Serialization Formats
13.2.
Using joblib
13.2.1.
Saving Models
13.2.2.
Loading Models
13.2.3.
Compression Options
13.2.4.
Memory Mapping
13.3.
Using pickle
13.3.1.
Python Object Serialization
13.3.2.
Compatibility Considerations
13.3.3.
Security Concerns
13.4.
Version Compatibility
13.4.1.
Scikit-learn Version Changes
13.4.2.
Python Version Compatibility
13.4.3.
Dependency Management
13.5.
Model Deployment Strategies
13.5.1.
Batch Prediction
13.5.2.
Real-time Prediction
13.5.3.
API Integration
13.5.4.
Cloud Deployment
13.6.
Model Monitoring
13.6.1.
Performance Tracking
13.6.2.
Data Drift Detection
13.6.3.
Model Retraining
13.7.
Best Practices
13.7.1.
Model Versioning
13.7.2.
Documentation
13.7.3.
Testing Procedures
13.7.4.
Rollback Strategies
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12. Advanced Topics
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14. Performance Optimization