Welcome to Sijin’s Machine Learning Notes

Welcome to my page where I share notes on various machine learning algorithms that I encounter in my daily work.

The examples provided here draw mostly from classic machine learning books, which I have adapted to illustrate these algorithms.

• Deep Learning (by I.Goodfellow, Y.Bengio and A.Courville)

• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (by A.Géron)

• Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (by W.McKinney)

• The StatQuest Illustrated Guide to Machine Learning (by J.Starmer)

• Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python (by P.Bruce, A.Bruce and P.Gedeck)

Please note that ideas from various online resources have been utilized in creating these notes, and while I regret that I cannot list all of them here, I am grateful for their contributions.

Unlike many machine learning handbooks, this collection focuses solely on the methods that I have used in my work. My goal is to provide intuitive explanations of the different machine learning algorithms, using examples that are easy to understand and avoiding complicated mathematical equations as much as possible.

For more information, please feel free to contact me, Sijin, at zsjzyhzp@gmail.com.

Note

This project is under active development.

Contents

• Decision Tree
  • Growing trees
  • Random Forest
  • Adaboost
  • Gradient Boosting
  • XGBoost
• Neural network
  • Densely connected neural network
  • Covolutional neural network
  • Recurrent neural network
  • Generative Adversarial Network
  • Graph Neural Network
• Ensemble method
  • Introduction
  • Bagging
  • Boosting
  • Stacking
• Model evaluations and improvement
  • Model evaluation
  • Feature importance
  • Model improvement
• Example applications
  • Examples