Imbalanced Learning (Unbalanced Data) – The Complete Guide

Imbalanced Learning (Unbalanced Data) – The Complete Guide, available at $24.99, has an average rating of 4.65, with 67 lectures, based on 78 reviews, and has 878 subscribers.

You will learn about Understand the underline causes of the Class Imbalance problem Why it is a major challenge in machine learning and data mining fields Learn the different characteristics of imbalanced datasets Learn the state-of-the-art techniques and algorithms Understand variety of data based methods such as SMOTE, ADASYN, B-SMOTE and many more! Apply Data-Based Techniques in practice Understand different algorithmic approaches such as: One Class Learning, Cost Sensitive Learning and more! Apply Algorithmic-Based methods in practice Learn how to correctly evaluate a prediction model built using imbalanced data Learn strategies and recommendations to help you avoid pitfalls when working with imbalanced dataset This course is ideal for individuals who are This course is for students and professionals who are working in the machine learning / data science area and want to increase their knowledge and skills. It is also for students who are currently taking a course in these areas. It is not for students with no background knowledge in Machine Learning. It is particularly useful for This course is for students and professionals who are working in the machine learning / data science area and want to increase their knowledge and skills. It is also for students who are currently taking a course in these areas. It is not for students with no background knowledge in Machine Learning.

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Summary

Title: Imbalanced Learning (Unbalanced Data) – The Complete Guide

Price: $24.99

Average Rating: 4.65

Number of Lectures: 67

Number of Published Lectures: 61

Number of Curriculum Items: 67

Number of Published Curriculum Objects: 61

Original Price: $24.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the underline causes of the Class Imbalance problem
Why it is a major challenge in machine learning and data mining fields
Learn the different characteristics of imbalanced datasets
Learn the state-of-the-art techniques and algorithms
Understand variety of data based methods such as SMOTE, ADASYN, B-SMOTE and many more!
Apply Data-Based Techniques in practice
Understand different algorithmic approaches such as: One Class Learning, Cost Sensitive Learning and more!
Apply Algorithmic-Based methods in practice
Learn how to correctly evaluate a prediction model built using imbalanced data
Learn strategies and recommendations to help you avoid pitfalls when working with imbalanced dataset

Who Should Attend

This course is for students and professionals who are working in the machine learning / data science area and want to increase their knowledge and skills. It is also for students who are currently taking a course in these areas. It is not for students with no background knowledge in Machine Learning.

Target Audiences

This course is for students and professionals who are working in the machine learning / data science area and want to increase their knowledge and skills. It is also for students who are currently taking a course in these areas. It is not for students with no background knowledge in Machine Learning.

This is a niche topic for students interested in data science and machine learning fields. The classical data imbalance problem is recognized as one of the major problems in the field of data mining and machine learning. Imbalanced learning focuses on how an intelligent system can learn when it is provided with unbalanced data.

There is an unprecedented amount of data available. This has caused knowledge discovery to garner attention in recent years. However, many real-world datasets are imbalanced. Learning from unbalanced data poses major challenges and is recognized as needing significant attention.

The problem with unbalanced data is the performance of learning algorithms in the presence of underrepresented data and severely skewed class distributions. Models trained on imbalanced datasets strongly favor the majority class and largely ignore the minority class. Several approaches introduced to date present both data-based and algorithmic solutions.

The specific goals of this course are:

Help the students understand the underline causes of unbalanced data problem.
Go over the major state-of-the-art methods and techniques that you can use to deal with imbalanced learning.
Explain the advantages and drawback of different approaches and methods .
Discuss the major assessment metrics for imbalanced learning to help you correctly evaluate the effectiveness of your solution.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Problem Definition

Lecture 3: How Common is this problem?

Lecture 4: Prerequisites & Course Outcomes

Lecture 5: The Four Different Characteristics

Lecture 6: How Hard is my Unbalanced Dataset?

Lecture 7: Datasets – Quick Guide

Lecture 8: Languages & Source Code

Lecture 9: Installing Anaconda for Mac

Lecture 10: Installing Anaconda for Windows

Chapter 2: Data-based Approaches – Under-Sampling

Lecture 1: Data-based Approaches Introduction

Lecture 2: Undersampling Methods Introduction

Lecture 3: Undersampling: Random Undersampling

Lecture 4: Example – Random Undersampling

Lecture 5: Tomek Link

Lecture 6: Practical Example – Tomek Link

Lecture 7: UnderSampling: One Sided Selection

Lecture 8: Practical Example – OSS

Lecture 9: CPM: Class Purity Maximization

Lecture 10: SBC: Sampling Based on Clustering

Lecture 11: Practical Example – Clustering

Lecture 12: ENN Edited Nearest Neighbor

Lecture 13: Practical Example – ENN

Lecture 14: NearMiss-2

Lecture 15: Practical Example – NearMiss

Chapter 3: Data-based Approaches: Over-Sampling

Lecture 1: Oversampling

Lecture 2: Random Oversampling

Lecture 3: Practical Example – Random Oversampling

Lecture 4: SMOTE: Synthetic Minority Over-sampling Technique

Lecture 5: Practical Example – SMOTE

Lecture 6: B-SMOTE

Lecture 7: Practical Example – Borderline-SMOTE

Lecture 8: SMOTE-SL

Lecture 9: ADASYN – Adaptive Synthetic

Lecture 10: Practical Example – Adaptive Synthetic

Chapter 4: Data-based Approaches: Hybrid Techniques

Lecture 1: Hybrid Techniques

Lecture 2: Practical Example – SMOTE-ENN

Lecture 3: Practical Example – SMOTE-Tomek Link

Chapter 5: Algorithmic approach

Lecture 1: Algorithmic approach Introduction

Lecture 2: Cost Sensitive Learning

Lecture 3: Practical Example – Cost Sensitive Learning

Lecture 4: One-class Learning

Lecture 5: Active Learning

Chapter 6: Evaluation: Performance Measurements & Statistical Test

Lecture 1: Introduction

Lecture 2: Confusion Matrix

Lecture 3: Confusion Matrix Example

Lecture 4: Accuracy & Error Rate

Lecture 5: Accuracy & Error Rate Example

Lecture 6: Precision & Recall

Lecture 7: Precision & Recall Example

Lecture 8: F-measure, Adjusted F-measure & Geometric mean

Lecture 9: F1 Score Example

Lecture 10: Geometric Mean Score Example

Lecture 11: ROC (AUC)

Lecture 12: ROC AUC Score Example

Lecture 13: Iman-Davenport & Wilcoxon Paired Signed-Rank Tests

Chapter 7: Extra – General Topics Unbalanced Data Prospective

Lecture 1: Overfitting & Underfitting

Lecture 2: Train/Test Split (Unbalanced Data)

Lecture 3: Validation Set

Lecture 4: Cross Validation

Chapter 8: Recommendations & Strategies

Lecture 1: Final Remarks & Recommended Strategies

Instructors

Bassam Almogahed
Machine Learning Specialist

Rating Distribution

1 stars: 3 votes
2 stars: 2 votes
3 stars: 10 votes
4 stars: 19 votes
5 stars: 44 votes

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