Logistic Regression-Everything you need to know before starting this course
About this course
Welcome
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Optional: Download data assets
Logistic Regression-Logistic Regression: Introduction to Classification and Error Metrics
Introduction: What is Classification?
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Introduction to Logistic Regression
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Classification with Logistic Regression
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Logistic Regression with Multi-Classes
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Implementing Logistic Regression Models
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Confusion Matrix, Accuracy, Specificity, Precision, and Recall
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Classification Error Metrics: ROC and Precision-Recall Curves
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Implementing the Calculation of ROC and Precision-Recall Curves
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Logistic Regression-Logistic Regression Labs
[Optional] Download Assets for Demo Lab: Logistic Regression
[Optional] Logistic Regression Lab - Part 1
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[Optional] Logistic Regression Lab - Part 2
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[Optional] Logistic Regression Lab - Part 3
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Logistic Regression-End of module review & evaluation
Summary/Review
K Nearest Neighbors-K Nearest Neighbors
K Nearest Neighbors for Classification
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K Nearest Neighbors Decision Boundary
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K Nearest Neighbors Distance Measurement
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K Nearest Neighbors Pros and Cons
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K Nearest Neighbors with Feature Scaling
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K Nearest Neighbors-K Nearest Neighbors Labs
[Optional] K Nearest Neighbors Notebook - Part 1
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[Optional] K Nearest Neighbors Notebook - Part 2
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[Optional] K Nearest Neighbors Notebook - Part 3
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K Nearest Neighbors-End of module review & evaluation
Summary/Review
Support Vector Machines-Support Vector Machines
Introduction to Support Vector Machines
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Classification with Support Vector Machines
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The Support Vector Machines Cost Function
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Regularization in Support Vector Machines
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Support Vector Machines-Support Vector Machines Kernels
Introduction to Support Vector Machines Gaussian Kernels
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Support Vector Machines Gaussian Kernels - Part 1
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Support Vector Machines Gaussian Kernels - Part 2
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Support Vector Machines Workflow
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Implementing Support Vector Machines Kernal Models
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Support Vector Machines-Support Vector Machines Labs
[Optional] Support Vector Machines Notebook - Part 1
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[Optional] Support Vector Machines Notebook - Part 2
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[Optional] Support Vector Machines Notebook - Part 3
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Support Vector Machines-End of module review
Summary/Review
Decision Trees-Decision Trees
Overview of Classifiers
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Introduction to Decision Trees
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Building a Decision Tree
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Entropy-based Splitting
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Other Decision Tree Splitting Criteria
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Pros and Cons of Decision Trees
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Decision Trees-Decision Trees Labs
[Optional] Download Assets for Demo Lab: Decision Trees
[Optional] Decision Trees Notebook - Part 1
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[Optional] Decision Trees Notebook - Part 2
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[Optional] Decision Trees Notebook - Part 3
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Decision Trees-End of module review
Summary/Review
Ensemble Models-Ensemble Based Methods and Bagging
Ensemble Based Methods and Bagging - Part 1
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Ensemble Based Methods and Bagging - Part 2
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Ensemble Based Methods and Bagging - Part 3
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Ensemble Models-Random Forest
Random Forest
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Ensemble Models-Bagging Labs
[Optional] Download Assets for Demo Lab: Bagging
[Optional] Bagging Notebook - Part 1
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[Optional] Bagging Notebook - Part 2
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[Optional] Bagging Notebook - Part 3
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Ensemble Models-Boosting and Stacking
Review of Bagging
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Overview of Boosting
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Adaboost and Gradient Boosting Overview
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Adaboost and Gradient Boosting Syntax
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Stacking
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Ensemble Models-Boosting and Stacking Labs
[Optional] Download Assets for Demo Lab: Boosting and Stacking
[Optional] Boosting Notebook - Part 1
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[Optional] Boosting Notebook - Part 2
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[Optional] Boosting Notebook - Part 3
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Ensemble Models-End of module review & evaluation
Summary/Review
Modeling Unbalanced Classes-Model Interpretability
Model Interpretability
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Examples of Self-Interpretable and Non-Self-Interpretable Models
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Model-Agnostic Explanations
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Surrogate Models
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Modeling Unbalanced Classes-Modeling Unbalanced Classes
Introduction to Unbalanced Classes
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Upsampling and Downsampling
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Modeling Approaches: Weighting and Stratified Sampling
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Modeling Approaches: Random and Synthetic Oversampling
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Modeling Approaches: Nearing Neighbor Methods
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Modeling Approaches: Blagging
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Modeling Unbalanced Classes-End of Module Review
Summary/Review