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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
Building Machine Learning Pipelines
Pipeline Concepts
Benefits of Pipelines
Data Leakage Prevention
Code Organization
Reproducibility
The Pipeline Class
Creating Pipelines
Pipeline Steps
Chaining Transformers and Estimators
Named Steps
Pipeline Methods
The ColumnTransformer
Column-specific Transformations
Handling Mixed Data Types
Remainder Parameter
Sparse Matrix Handling
Feature Union
Parallel Feature Processing
Combining Features
Pipeline with Preprocessing
Standard Preprocessing Pipeline
Categorical and Numerical Features
Missing Value Handling
Pipeline with Feature Selection
Integrated Feature Selection
SelectFromModel
RFE in Pipelines
Pipeline Hyperparameter Tuning
Parameter Naming Conventions
Grid Search with Pipelines
Nested Cross-Validation
Custom Transformers
Creating Custom Transformers
BaseEstimator and TransformerMixin
fit and transform Methods
Pipeline Persistence
Saving Complete Pipelines
Loading and Using Saved Pipelines
Pipeline Debugging
Intermediate Results
Step-by-step Execution
Memory Usage
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11. Working with Text Data