GitHub - dcanguven/inside-machine-learning: Exploring machine learning algorithms, model behavior and from-scratch implementations.

A collection of technical notebooks exploring machine learning algorithms through experimentation and from-scratch implementations.

The notebooks explore topics such as optimization, model behavior, algorithm mechanics, and implementation details behind high-level libraries.

Written in a simple and direct style with an emphasis on clarity and analytical thinking.

Most of the topics in this repository are also covered in more detail on Medium.

Focus

Additional notebooks and technical studies will continue to be added over time.

Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
01. Boosting Algorithm From Scratch.ipynb		01. Boosting Algorithm From Scratch.ipynb
02. Decision Tree From Scratch.ipynb		02. Decision Tree From Scratch.ipynb
03. Bias-Variance Trade-Off Explained.ipynb		03. Bias-Variance Trade-Off Explained.ipynb
04. Principal Component Analysis From Scratch.ipynb		04. Principal Component Analysis From Scratch.ipynb
05. My Kaggle Competition Approach for Predict Calorie Expenditure.ipynb		05. My Kaggle Competition Approach for Predict Calorie Expenditure.ipynb
06. Target Leakage Explained.ipynb		06. Target Leakage Explained.ipynb
07 . R² Improved from 0.952 to 0.999 - Benchmarking ML Regressors.ipynb		07 . R² Improved from 0.952 to 0.999 - Benchmarking ML Regressors.ipynb
08. Decision Tree – Different Approach for Thresholds.ipynb		08. Decision Tree – Different Approach for Thresholds.ipynb
09. Support Vector Machine from Scratch.ipynb		09. Support Vector Machine from Scratch.ipynb
README.md		README.md