# Machine Learning and Data Science Knowledge Base Welcome to my Obsidian notes on ML/DS. I have spent many years passively collecting knowledge in the form of text snippets and links and recently decided to go through all of it and structure it. This obsidian vault will have notes about general ML theory, the approaches to specific business cases, interview preparations and other things. For now, it is quite sparse, but I plan to regularly update it. ## Core Machine Learning - [[Decision Tree|Decision Trees]] - [[Random Forest]] - [[Gradient boosting]] - [[Linear Regression]] - [[Logistic regression]] - [[K-Nearest Neighbors]] - [[K-means clustering]] - [[SVM]] ## Model Evaluation and Improvement - Metrics - [[Regularization]] - [[Validation]] - [[Bias-Variance Trade-off]] ## General - [[Interview preparation]] The notes are available in my [Github repository](https://github.com/Erlemar/dswok).