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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