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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
12.
Advanced Topics
12.1.
Ensemble Methods
12.1.1.
Bagging
12.1.1.1.
Bootstrap Aggregating
12.1.1.2.
Variance Reduction
12.1.2.
Boosting
12.1.2.1.
Sequential Learning
12.1.2.2.
Bias Reduction
12.1.3.
Stacking
12.1.3.1.
Meta-learning
12.1.3.2.
Blending
12.1.4.
Voting Classifiers and Regressors
12.1.4.1.
Hard Voting
12.1.4.2.
Soft Voting
12.2.
Multi-output Learning
12.2.1.
Multi-output Regression
12.2.2.
Multi-output Classification
12.2.3.
Multi-label Classification
12.2.4.
Classifier Chains
12.3.
Semi-supervised Learning
12.3.1.
Label Propagation
12.3.2.
Label Spreading
12.3.3.
Self-training
12.4.
Online Learning
12.4.1.
Incremental Learning
12.4.2.
Partial Fit Methods
12.4.3.
Stream Processing
12.5.
Calibration
12.5.1.
Probability Calibration
12.5.2.
Platt Scaling
12.5.3.
Isotonic Regression
12.6.
Feature Engineering Automation
12.6.1.
Automated Feature Selection
12.6.2.
Feature Construction
12.7.
Model Interpretation
12.7.1.
Feature Importance
12.7.2.
Permutation Importance
12.7.3.
Partial Dependence Plots
12.7.4.
SHAP Values Integration
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11. Working with Text Data
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13. Model Persistence and Deployment