Machince Learning 46
- Hands-on 07. SHAP
- Hands-on 06. Isolation Forest
- Hands-on 05. Random Forest
- Hands-on 04. BallTree
- Hands-on 03. DBSCAN
- Hands-on 02. PCA
- Hands-on 01. KMeans
- Manifold Learning, MDS, and PCA
- K-Means
- Unsupervised Learning
- Support Vector Machines with Kernels
- Loss Function
- Support Vector Machine (SVM) — Soft Margin with Slack Variables
- Support Vector Machine (SVM)
- Bagged Trees & Random Forests
- Bagging & Ensemble
- Bootstrapping
- Decision Trees
- Pruning of Decision Trees
- Decision Trees
- Regularization
- Overfitting
- Naive Bayes
- Evaluating Classification
- Logistic vs LDA
- Extension of Linear Discriminant Analysis
- Linear Discriminant Analysis
- Discriminant Analysis
- Bayes Classifier
- Extension of Logistic Regression
- Stochastic Gradient Descent
- Optimization and Gradient Descent
- MLE for Logistic Regression
- Logistic Regression
- Entry of Classification, Linear Regression vs Logistic Regression
- Feature Engineering
- Statistical Interpret for MLE
- Statistical Properties for Linear Regression
- MLE for Linear Regression
- MLE Example
- Likelihood and MLE
- Linear Regression
- Supervised Learning Process
- Entry of Machine Learning
- Model Evaluation
- Feature Selection