Snack
Handling Imbalanced Data in Machine Learning
Core and advanced techniques for imbalanced datasets — undersampling, oversampling (SMOTE, ADASYN), cost-sensitive learning, ensembles, and specialized metrics — using motion graphics and Python.
4.4
What you'll learn
Grasp class imbalance challenges, base rate fallacies, and accuracy paradoxes
Master appropriate evaluation metrics (Precision, Recall, F1-Score, PR-AUC, ROC-AUC)
Apply random undersampling, Tomek Links, and Edited Nearest Neighbours
Implement oversampling techniques (Random Oversampling, SMOTE, Borderline-SMOTE, ADASYN)
Master hybrid resampling methods (SMOTEENN, SMOTETomek) to clean noisy boundaries
Utilize cost-sensitive learning algorithms (weighted loss functions, class weights)
Build imbalanced ensemble models (Balanced Random Forest, EasyEnsemble, RUSBoost)
Avoid data leakage by integrating resampling into Scikit-Learn pipelines
Apply probability threshold moving to optimize business-specific cost tradeoffs
Build complete end-to-end imbalanced classification workflows in Python
This course includes
- 10 hours on-demand video
- 16 articles
- 56 downloadable resources
- Access on mobile and TV
- Closed captions
- Certificate of completion
Course content
8 sectionsRequirements
- Basic familiarity with Python, Pandas, and Scikit-Learn
- Fundamental understanding of machine learning classification concepts
- High school level algebra and basic statistics
- No prior experience with imbalanced data handling is required
Secret Sauce
Why this course works
Description
Are you aspiring to work as a Data Scientist, Machine Learning Engineer, Fraud Risk Analyst, or Medical AI Specialist? Do you want to solve real-world classification problems where rare events — like fraud, rare disease diagnosis, or customer churn — are heavily outnumbered by normal cases?
Machine Learning with Imbalanced Data, Visualized is designed specifically to help you overcome the accuracy paradox and build high-performing models on severely skewed datasets using custom motion graphics, animated decision boundary shifts, and production-ready Python pipelines. Every lesson uses fully animated motion graphics to show you exactly how SMOTE interpolates synthetic feature space and how precision-recall curves respond to class skew.
Fully animatedHighly intuitiveFully comprehensiveDirect and conciseImbalanced-Learn & Scikit-LearnRich in coding exercisesBuilt on rare-event intuition
Imbalanced data techniques are scattered across obscure documentation and research papers, but a structured program that clearly explains when to oversample, when to adjust cost matrices, and how to evaluate real-world trade-offs is hard to find. Software libraries automate the execution — this course equips you with something far more essential: algorithmic decision-making. We spent months crafting custom motion graphics for this program, so your enrollment includes animated boundary diagrams, structured notebooks, and a dedicated glossary of resampling techniques.
What sets this course apart
Custom Motion Graphics
100% animated visual lessons that break down complex resampling algorithms into clear spatial movements.
Expert Instruction
Led by a seasoned data scientist and risk modeling practitioner.
Complete Toolkit
Covers undersampling, oversampling, hybrid methods, cost-sensitive learning, and imbalanced ensembles.
Practical Code
Production-ready Python scripts using Imbalanced-Learn and Scikit-Learn alongside every lesson.
Responsive Support
Get answers to your technical questions within one business day.
Efficient Pacing
Tightly edited, zero-fluff video designed to maximize learning speed.
Why master these skills
Real-World Applicability
Almost every critical business problem, from fraud detection to medical diagnostics, involves imbalanced data.
Prevents Costly Failures
Standard accuracy hides critical errors; class balance techniques prevent costly false negatives.
High-Demand Expertise
Organizations actively seek professionals who can build reliable models when positive targets are rare.
Production Reliability
Embedding resampling correctly within cross-validation prevents leakage and ensures stability.
This course is backed by Otomas' 30-day money-back guarantee, giving you a risk-free opportunity to explore the material.
Who this course is for
- Data Scientists struggling with severe class imbalance, low recall, or deceptive accuracy
- Machine Learning Engineers building real-time fraud, anomaly, or risk detection engines
- Financial, Medical, and Cyber-Security Analysts working with rare-event detection
- Software Developers deploying predictive classification models into production
- Visual learners who want to see how decision boundaries shift during resampling
- Anyone looking for a complete, visually guided masterclass in Imbalanced Data Handling
4.4 course rating