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Курс: Applied Machine Learning in Python. Чтобы вернуться, нажмите здесь.
- Introduction
- Key Concepts in Machine Learning
- Python Tools for Machine Learning
- An Example Machine Learning Problem
- Examining the Data
- K-Nearest Neighbors Classification
- Introduction to Supervised Machine Learning
- Overfitting and Underfitting
- Supervised Learning: Datasets
- K-Nearest Neighbors: Classification and Regression
- Linear Regression: Least-Squares
- Linear Regression: Ridge, Lasso, and Polynomial Regression
- Logistic Regression
- Linear Classifiers: Support Vector Machines
- Multi-Class Classification
- Kernelized Support Vector Machines
- Cross-Validation
- Decision Trees
- Model Evaluation & Selection
- Confusion Matrices & Basic Evaluation Metrics
- Classifier Decision Functions
- Precision-recall and ROC curves
- Multi-Class Evaluation
- Regression Evaluation
- Model Selection: Optimizing Classifiers for Different Evaluation Metrics
- Naive Bayes Classifiers
- Random Forests
- Gradient Boosted Decision Trees
- Neural Networks
- Deep Learning (Optional)
- Data Leakage
- Introduction
- Dimensionality Reduction and Manifold Learning
- Clustering
- Conclusion