Imbalanced Classification Master Class in Python

Imbalanced Classification Master Class in Python, available at $39.99, has an average rating of 3.85, with 88 lectures, based on 16 reviews, and has 186 subscribers.

You will learn about How to use data sampling algorithms like SMOTE to transform the training dataset for an imbalanced dataset when fitting a range of machine learning models How algorithms from the field of cost-sensitive learning can be used for imbalanced classification How to use modified versions of standard algorithms like SVM and decision trees to take the class weighting into account How to tune the threshold when interpreting predicted probabilities as class labels How to calibrate probabilities predicted by nonlinear algorithms that are not fit using a probabilistic framework How to use algorithms from the field of outlier detection and anomaly detection for imbalanced classification How to use modified ensemble algorithms that have been modified to take the class distribution into account during training How to systematically work through an imbalanced classification predictive modeling project This course is ideal for individuals who are If you're studying to be a machine learning engineer, this course is for you. or If you are a machine learning engineer, this course is for you. or If you're a data scientist moving to machine learning, this course is for you. It is particularly useful for If you're studying to be a machine learning engineer, this course is for you. or If you are a machine learning engineer, this course is for you. or If you're a data scientist moving to machine learning, this course is for you.

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Summary

Title: Imbalanced Classification Master Class in Python

Price: $39.99

Average Rating: 3.85

Number of Lectures: 88

Number of Published Lectures: 88

Number of Curriculum Items: 88

Number of Published Curriculum Objects: 88

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

How to use data sampling algorithms like SMOTE to transform the training dataset for an imbalanced dataset when fitting a range of machine learning models
How algorithms from the field of cost-sensitive learning can be used for imbalanced classification
How to use modified versions of standard algorithms like SVM and decision trees to take the class weighting into account
How to tune the threshold when interpreting predicted probabilities as class labels
How to calibrate probabilities predicted by nonlinear algorithms that are not fit using a probabilistic framework
How to use algorithms from the field of outlier detection and anomaly detection for imbalanced classification
How to use modified ensemble algorithms that have been modified to take the class distribution into account during training
How to systematically work through an imbalanced classification predictive modeling project

Who Should Attend

If you're studying to be a machine learning engineer, this course is for you.
If you are a machine learning engineer, this course is for you.
If you're a data scientist moving to machine learning, this course is for you.

Target Audiences

If you're studying to be a machine learning engineer, this course is for you.
If you are a machine learning engineer, this course is for you.
If you're a data scientist moving to machine learning, this course is for you.

Welcome to Imbalanced Classification Master Class in Python.

Classification predictive modeling is the task of assigning a label to an example. Imbalanced classification is those classification tasks where the distribution of examples across the classes is not equal. Typically the class distribution is severely skewed so that for each example in the minority class, there may be one hundred or even one thousand examples in the majority class. Practical imbalanced classification requires the use of a suite of specialized techniques, data preparation techniques, learning algorithms, and performance metrics.

Let’s discuss what you’ll learn in this course.

The challenge and intuitions for imbalanced classification datasets.
How to choose an appropriate performance metric for evaluating models for imbalanced classification.
How to appropriately stratify an imbalanced dataset when splitting into train and test sets and when using k-fold cross-validation.
How to use data sampling algorithms like SMOTE to transform the training dataset for an imbalanced dataset when fitting a range of standard machine learning models.
How algorithms from the field of cost-sensitive learning can be used for imbalanced classification.
How to use modified versions of standard algorithms like SVM and decision trees to take the class weighting into account.
How to tune the threshold when interpreting predicted probabilities as class labels.
How to calibrate probabilities predicted by nonlinear algorithms that are not fit using a probabilistic framework.
How to use algorithms from the field of outlier detection and anomaly detection for imbalanced classification.
How to use modified ensemble algorithms that have been modified to take the class distribution into account during training.
How to systematically work through an imbalanced classification predictive modeling project.

This course was created to be completed linearly, from start to finish. That being said, if you know the basics and need help with a specific method or type of problem, then you can flip straight to that section and get started. This course was designed for you to completed on your laptop or desktop, on the screen, not on a tablet.

My hope is that you have the course open right next to your editor and run the examples as you read about them. This course is not intended to be completed passively or be placed in a folder as a reference text. It is a playbook, a workbook, and a guidebook intended for you to learn by doing and then apply your new understanding with working Python examples. To get the most out of the course, I would recommend playing with the examples in each tutorial. Extend them, break them, then fix them.

Thanks for you interest in Imbalanced Classification Master Class in Python.

Now let’s get started!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Course Outcomes

Lecture 3: Course Structure

Lecture 4: Imbalanced Classification Defined

Lecture 5: Causes of Class Imbalance

Lecture 6: Challenge of Imbalance Classification

Lecture 7: Examples of Class Imbalance

Chapter 2: Understanding Class Imbalance

Lecture 1: Create Synthetic Dataset with Class Distribution

Lecture 2: Effect of Skewed Class Distributions

Lecture 3: Visualizing Extreme Skew

Lecture 4: Why Imbalanced Classification Is Hard

Lecture 5: Compounding Effect of Dataset Size

Lecture 6: Compounding Effect of Label Noise

Lecture 7: Compounding Effect of Data Distribution

Chapter 3: Model Evaluation

Lecture 1: Evaluation Metrics and Imbalance

Lecture 2: Taxonomy of Classifier Evaluation Metrics

Lecture 3: Ranking Metrics for Imbalanced Classification

Lecture 4: Probabilistic Metrics for Imbalanced Classification

Lecture 5: How to Choose an Evaluation Metric

Lecture 6: Accuracy Fails for Imbalanced Classification

Lecture 7: Accuracy Paradox

Lecture 8: Demo: Accuracy for Imbalanced Classification

Lecture 9: Precision for Imbalanced Classification

Lecture 10: Precision for Multi-Class Classification

Lecture 11: Recall for Imbalanced Classification

Lecture 12: Demo: Recall for Imbalanced Classification

Lecture 13: F-Measure for Imbalanced Classification

Lecture 14: Demo: F- Measure for Imbalanced Classification

Lecture 15: ROC Curves and Precision-Recall Curves

Lecture 16: ROC Curve

Lecture 17: Demo: ROC Curve

Lecture 18: ROC Area Under Curve (AUC) Score

Lecture 19: Precision-Recall Curves

Lecture 20: Precision-Recall Area Under Curve (AUC) Score

Lecture 21: ROC AUC on with Severe Imbalance

Lecture 22: ROC and Precision-Recall Curves With a Severe Imbalance

Lecture 23: Probability Scoring Methods in Python

Lecture 24: Log Loss Score

Lecture 25: Brier Score

Lecture 26: Cross-Validation for Imbalanced Classification

Lecture 27: Challenge of Evaluating Classifiers

Lecture 28: Failure of k-Fold Cross-Validation

Chapter 4: Data Sampling

Lecture 1: Data Sampling Methods for Imbalanced Classification

Lecture 2: Oversampling Techniques

Lecture 3: Undersampling Techniques

Lecture 4: Combinations of Techniques

Lecture 5: Random Resampling Imbalanced Datasets

Lecture 6: Demo: Random Oversampling Imbalanced Datasets

Lecture 7: Demo: Random Undersampling Imbalanced Datasets

Lecture 8: Demo: Combining Random Oversampling and Undersampling Techniques

Lecture 9: Synthetic Minority Oversampling Technique (SMOTE)

Lecture 10: SMOTE for Balancing Data

Lecture 11: SMOTE for Classification

Lecture 12: Borderline-SMOTE SVM

Lecture 13: Adaptive Synthetic Sampling (ADASYN)

Lecture 14: Undersampling Methods

Lecture 15: Near Miss Undersampling (NearMiss-1)

Lecture 16: Near Miss Undersampling (NearMiss-2 and NearMiss-3)

Lecture 17: Condensed Nearest Neighbor Rule Undersampling

Lecture 18: Tomek Links for Undersampling

Lecture 19: Edited Nearest Neighbors Rule for Undersampling (ENN)

Lecture 20: Neighborhood Cleaning Rule for Undersampling

Chapter 5: Cost-Sensitive Learning

Lecture 1: Cost-Sensitive Learning for Imbalanced Classification

Lecture 2: Not All Classification Errors Are Equal

Lecture 3: Cost-Sensitive Learning

Lecture 4: Cost-Sensitive Imbalanced Classification

Lecture 5: Cost-Sensitive Methods

Lecture 6: Cost-Sensitive Algorithms

Lecture 7: Cost-Sensitive Ensembles

Lecture 8: Cost-Sensitive Logistic Regression

Lecture 9: Logistic Regression for Imbalanced Classification

Lecture 10: Weighted Logistic Regression with Scikit-Learn

Lecture 11: Grid Search Weighted Logistic Regression

Lecture 12: Cost-Sensitive Decision Trees for Imbalanced Classification

Lecture 13: Decision Trees for Imbalanced Classification

Lecture 14: Weighted Decision Tree With Scikit-Learn

Lecture 15: Grid Search Weighted Decision Tree

Lecture 16: Develop a Cost-Sensitive Neural Network for Imbalanced Classification

Lecture 17: Neural Network Model in Keras

Lecture 18: Deep Learning for Imbalanced Classification

Lecture 19: Weighted Neural Network With Keras

Chapter 6: Projects

Lecture 1: Project: Breast Cancer Dataset

Lecture 2: Haberman Breast Cancer Survival Dataset

Lecture 3: Dataset Exploration

Lecture 4: Model Test and Baseline Result

Lecture 5: Evaluate Probabilistic Models

Lecture 6: Model Evaluation With Scaled Inputs

Lecture 7: Model Evaluation With Power Transform

Instructors

Mike West
Creator of LogikBot

Rating Distribution

1 stars: 1 votes
2 stars: 1 votes
3 stars: 3 votes
4 stars: 3 votes
5 stars: 8 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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