Quick Introduction

Theoretical Knowledge

Check out the notes (PDF files), which are some theoretical knowledge about machine learning algorithms summarized by myself.
The features of these notes are refined, organized and easy-understanding.

Name		Name	Last commit message	Last commit date
Latest commit History 136 Commits
Codes		Codes
Python_Machine_Learning_Book (3rd edition)		Python_Machine_Learning_Book (3rd edition)
images		images
01 Introduction.pdf		01 Introduction.pdf
02 Basics of ML.pdf		02 Basics of ML.pdf
03 Linear Regression.pdf		03 Linear Regression.pdf
04 Logistic Regression.pdf		04 Logistic Regression.pdf
05 Decision Tree.pdf		05 Decision Tree.pdf
06 Support Vector Machine.pdf		06 Support Vector Machine.pdf
07 Bayes Algorithm.pdf		07 Bayes Algorithm.pdf
08 Clustering Algorithm.pdf		08 Clustering Algorithm.pdf
09 K Nearest Neighbors .pdf		09 K Nearest Neighbors .pdf
10 PCA.pdf		10 PCA.pdf
11 Integrated Algorithm.pdf		11 Integrated Algorithm.pdf
12 XGBoost.ipynb		12 XGBoost.ipynb
12 XGBoost.pdf		12 XGBoost.pdf
README.md		README.md