Journal-based practice
Learn Machine Learning from Real Journal Case Studies
Understand how machine learning is used in real research, then relearn it visually, simply, and interactively.
Main case studies
Two focused paper-to-practice paths.
Start from the paper summary, then open the practice path when you are ready.
AI-Based Obesity Risk and Meal Planning
This case study explores how machine learning can predict obesity risk from eating habits, physical condition, and lifestyle, then connect the prediction to practical meal planning.
Paper sourceSpringer Endocrine · Predicting risk of obesity and meal planning to reduce obesity in adulthood using artificial intelligence
Data preprocessingClassificationRandom ForestXGBoost / Gradient BoostingSVM / KNN comparisonModel evaluationFeature importanceMeal planning recommendation
Open case study
PCA and LDA for Breast Cancer Detection
This case study explains how PCA and LDA reduce the Wisconsin Breast Cancer dataset and help classification models work with a cleaner feature space.
Paper sourceScienceDirect / Procedia Computer Science · Enhancing Breast Cancer Detection with Dimensionality Reduction Techniques: A Study Using PCA and LDA on Wisconsin Breast Cancer Data
PCALDADimensionality reductionFeature extractionClassificationModel performance comparisonMedical dataset analysis
Open case study
Paper to practice
A guided way to read research without getting lost.
Each case study turns a paper into a learning path beginners can follow.
- 01Read the research problem
- 02Identify the dataset
- 03Understand features and target
- 04Study the model
- 05Evaluate the results
- 06Rebuild it in the lab
- 07Take the insight
Ready for practice?
Summaries are free. Step-by-step practice lives in Learning Packages.
Project Access unlocks the complete visual path, related labs, and supporting files when available.
View Learning Packages