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Computer Science
Artificial Intelligence
Machine Learning
Machine Learning for Developers
1. Introduction to Machine Learning for Developers
2. Machine Learning Project Lifecycle
3. Supervised Learning Fundamentals
4. Unsupervised Learning Fundamentals
5. Python Machine Learning Ecosystem
6. Data Engineering for Machine Learning
7. Pre-trained Models and Transfer Learning
8. Model Deployment and MLOps
9. Production Monitoring and Maintenance
10. Natural Language Processing for Developers
11. Computer Vision for Developers
12. Responsible AI and Ethics
13. Advanced Topics and Specializations
Responsible AI and Ethics
Bias and Fairness
Types of Bias
Historical Bias
Representation Bias
Measurement Bias
Evaluation Bias
Fairness Metrics
Individual Fairness
Group Fairness
Counterfactual Fairness
Bias Mitigation
Pre-processing Techniques
In-processing Techniques
Post-processing Techniques
Model Interpretability
Interpretability vs Explainability
Global Interpretability
Feature Importance
Model-Agnostic Methods
Model-Specific Methods
Local Interpretability
LIME
SHAP
Counterfactual Explanations
Interpretability Tools
What-If Tool
InterpretML
Alibi
Privacy and Security
Data Privacy
Anonymization Techniques
Differential Privacy
Federated Learning
Model Security
Adversarial Attacks
Model Inversion
Membership Inference
Secure Deployment
Encrypted Inference
Secure Multi-Party Computation
Homomorphic Encryption
Regulatory Compliance
GDPR Compliance
AI Act Compliance
Industry-Specific Regulations
Documentation Requirements
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13. Advanced Topics and Specializations