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Cours : Supervised Learning: Classification. Cliquez sur
ici
pour revenir en arrière.
Welcome
Optional: How to create a project in IBM Watson Studio
Introduction: What is Classification?
Introduction to Logistic Regression
Classification with Logistic Regression
Confusion Matrix, Accuracy, Specificity, Precision, and Recall
Classification Error Metrics: ROC and Precision-Recall Curves
Logistic Regression Lab - Part 1
Logistic Regression Lab - Part 2
Logistic Regression Lab - Part 3
K Nearest Neighbors for Classification
K Nearest Neighbors Decision Boundary
K Nearest Neighbors Distance Measurement
K Nearest Neighbors with Feature Scaling
K Nearest Neighbors Notebook - Part 1
K Nearest Neighbors Notebook - Part 2
K Nearest Neighbors Notebook - Part 3
Introduction to Support Vector Machines
Classification with Support Vector Machines
The Support Vector Machines Cost Function
Regularization in Support Vector Machines
Introduction to Support Vector Machines Gaussian Kernels
Support Vector Machines Gaussian Kernels - Part 1
Support Vector Machines Gaussian Kernels - Part 2
Implementing Support Vector Machines Kernel Models
Support Vector Machines Notebook - Part 1
Support Vector Machines Notebook - Part 2
Support Vector Machines Notebook - Part 3
Introduction to Decision Trees
Building a Decision Tree
Entropy-based Splitting
Other Decision Tree Splitting Criteria
Pros and Cons of Decision Trees
Decision Trees Notebook - Part 1
Decision Trees Notebook - Part 2
Decision Trees Notebook - Part 3
Ensemble Based Methods and Bagging - Part 1
Ensemble Based Methods and Bagging - Part 2
Ensemble Based Methods and Bagging - Part 3
Random Forest
Bagging Notebook - Part 1
Bagging Notebook - Part 2
Bagging Notebook - Part 3
Review of Bagging
Overview of Boosting
Adaboost and Gradient Boosting Overview
Adaboost and Gradient Boosting Syntax
Stacking
Boosting Notebook - Part 1
Boosting Notebook - Part 2
Boosting Notebook - Part 3
Introduction to Unbalanced Classes
Upsampling and Downsampling
Modeling Approaches: Weighting and Stratified Sampling
Modeling Approaches: Random and Synthetic Oversampling
Modeling Approaches: Nearing Neighbor Methods
Modeling Approaches: Blagging