#Machine Learning
#Supervised Learning
#Unsupervised Learning
Machine Learning Codebook
is a tutorial of machine learning for those who are studying Machine Learning. This repo deals with machine learning intro and supervised, unsupervised learning. Most of the contents have explanation of model and example codes.
- Much easier to read if you open codebook with Google colab
- 1-1. Machine Learning Introduction
- Contents
- What is Machine Learning?
- Essential Concepts
- Types of Machine Learning
- Overall Process of machine learning proejct
- Extra tips
- Colab version: Machine Learning Introduction.ipynb
- Contents
- 2-1. Regression
- Contents
- Linear Regression
- Gradient Descent (Batch Gradient Descent)
- Stochastic Gradient Basecent
- Mini-batch Gradient Descent
- Polynomial Regression
- Regularized Linear Models (Lasso)
- Regularized Linear Models (Lasso)
- Elastic Net
- Softmax Regression (Multinomial Lostic Regression)
- SVM Regressor
- Colab version: Regression.ipynb
- Contents
- 2-2. Classification
- Contents
- Logistic Regression
- Linear Support Vector Model
- Non-Linear Support Vector Model
- SGD Classifier
- XGBoost Classifier
- Colab version: Classification.ipynb
- Contents
- 2-3. Decision Tree
- Colab version: Decision Tree.ipynb
- 2-4. Ensemble
- Contents
- Voting
- Bagging
- Boosting (XGBoost, LightGBM etc)
- Pasting
- Pasting
- Colab version: Ensemble.ipynb
- Contents
- 3-1. Dimensionality Reduction
- Contents
- Prinipal Component analysis (PCA)
- T-sne
- Factor Analysis
- Colab version: Dimensionality Reduction.ipynb
- Contents
- 3-2. Clustering
- Contents
- K-means clustering
- Hierarchical Clustering
- DBSCAN
- Gasussian Mixture model (GMM)
- Colab version: Clustering.ipynb
- Contents
- Hyun Woo Jung (Hyun)
- Author Email : chopin_liszt@naver.com