Répertoire
Rechercher :
Vídeos
Cours : Supervised Learning: Classification. Cliquez sur ici pour revenir en arrière.
- About this course
- Welcome
- Optional: Introduction to IBM Watson Studio
- Optional: Overview of IBM Watson Studio
- Optional: How to create a project in IBM Watson Studio
- Optional: Download data assets
- 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 Demo (Activity)
- Logistic Regression Lab - Part 1
- Logistic Regression Lab - Part 2
- Logistic Regression Lab - Part 3
- Summary/Review
- K Nearest Neighbors for Classification
- K Nearest Neighbors Decision Boundary
- K Nearest Neighbors Distance Measurement
- K Nearest Neighbors with Feature Scaling
- K Nearest Neighbors Demo (Activity)
- K Nearest Neighbors Notebook - Part 1
- K Nearest Neighbors Notebook - Part 2
- K Nearest Neighbors Notebook - Part 3
- Summary/Review
- 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 Demo (Activity)
- Support Vector Machines Notebook - Part 1
- Support Vector Machines Notebook - Part 2
- Support Vector Machines Notebook - Part 3
- Summary/Review
- Introduction to Decision Trees
- Building a Decision Tree
- Entropy-based Splitting
- Other Decision Tree Splitting Criteria
- Pros and Cons of Decision Trees
- Decision Trees Demo (Activity)
- Decision Trees Notebook - Part 1
- Decision Trees Notebook - Part 2
- Decision Trees Notebook - Part 3
- Summary/Review
- Ensemble Based Methods and Bagging - Part 1
- Ensemble Based Methods and Bagging - Part 2
- Ensemble Based Methods and Bagging - Part 3
- Random Forest
- Bagging Demo (Activity)
- 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 and Stacking Demo (Activity)
- Boosting Notebook - Part 1
- Boosting Notebook - Part 2
- Boosting Notebook - Part 3
- Summary/Review
- 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
- Summary/Review