Linear algebra - intuitive explanations of matrices and determinants

1. Understanding matrices intuitively, part 1
2. Understanding matrices intuitively, part 2, eigenvalues and eigenvectors
3. What's an intuitive way to think about the determinant? (Stackexchange post)
4. Math Insight

Precision-Recall - evaluation and adjustment

1. Plotting precision-recall with Scikit
2. Classifier with adjustable precision vs recall

Reading list for decision trees, ensemble methods, support vector meachines

Decision trees and ensemble methods

1. Ensemble Machine Learning Algorithms in Python with scikit-learn
2. Difference between "fully developed decision trees" and "shallow decision trees"?(Stackexchange post)
3. Decision tree or logistic regression
4. Two-class AdaBoost (Scikit-learn page)
5. Intuitive explanations of differences between Gradient Boosting Trees (GBM) & Adaboost
6. Decision trees in python with scikit-learn and pandas
7. Advanced modeling - Predict Customer Churn - Logistic Regression, Decision Tree and Random Forest
8. Decision Trees, Confusion Matrices and Precision Recall
9. Xgboost in Sagemaker - Walkthrough with examples

Support Vector Machines

# maximal margin SVM  #Lagrange constrained optimization and svm

1. An idiot's guide to SVM - MIT lecture slide
   
2. Support Vector Machines: Maximum Margin Classifiers – Piotr Mirowski's NYU lecture slides
3. SVM - Understanding the math - Duality and Lagrange multipliers
4. Constrained Optimization Using Lagrange Multipliers