Practice writing common ML algorithms using barebones Python

Contents:

• Linear Regression
• Gradient Descent
• Logistic Regression
• Linear Discriminant Analysis
• Classification and Regression Trees
• Naive Bayes
• Gaussian Naive Bayes
• k-Nearest Neighbors
• Learning Vector Quantization
• Support Vector Machine
• Bagging and Random Forest
• Boosting and AdaBost