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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
12.
Responsible AI and Ethics
12.1.
Bias and Fairness
12.1.1.
Types of Bias
12.1.1.1.
Historical Bias
12.1.1.2.
Representation Bias
12.1.1.3.
Measurement Bias
12.1.1.4.
Evaluation Bias
12.1.2.
Fairness Metrics
12.1.2.1.
Individual Fairness
12.1.2.2.
Group Fairness
12.1.2.3.
Counterfactual Fairness
12.1.3.
Bias Mitigation
12.1.3.1.
Pre-processing Techniques
12.1.3.2.
In-processing Techniques
12.1.3.3.
Post-processing Techniques
12.2.
Model Interpretability
12.2.1.
Interpretability vs Explainability
12.2.2.
Global Interpretability
12.2.2.1.
Feature Importance
12.2.2.2.
Model-Agnostic Methods
12.2.2.3.
Model-Specific Methods
12.2.3.
Local Interpretability
12.2.3.1.
LIME
12.2.3.2.
SHAP
12.2.3.3.
Counterfactual Explanations
12.2.4.
Interpretability Tools
12.2.4.1.
What-If Tool
12.2.4.2.
InterpretML
12.2.4.3.
Alibi
12.3.
Privacy and Security
12.3.1.
Data Privacy
12.3.1.1.
Anonymization Techniques
12.3.1.2.
Differential Privacy
12.3.1.3.
Federated Learning
12.3.2.
Model Security
12.3.2.1.
Adversarial Attacks
12.3.2.2.
Model Inversion
12.3.2.3.
Membership Inference
12.3.3.
Secure Deployment
12.3.3.1.
Encrypted Inference
12.3.3.2.
Secure Multi-Party Computation
12.3.3.3.
Homomorphic Encryption
12.4.
Regulatory Compliance
12.4.1.
GDPR Compliance
12.4.2.
AI Act Compliance
12.4.3.
Industry-Specific Regulations
12.4.4.
Documentation Requirements
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13. Advanced Topics and Specializations