Here you find posts and resources on machine learning foundations and traditional machine learning techniques. For posts on neural networks go to the deep learning section.
Machine Learning Foundations
- An Introduction to the Different Types of Machine Learning
- Understanding the Bias Variance Tradeoff and Machine Learning Models as Hypotheses
- Regularization in Machine Learning
- Residuals and the Least Squares Regression Line (Simple Introduction to Linear Regression)
- Ordinary Least Squares Regression (Technical Introduction to Linear Regression)
- The Coefficient of Determination and Linear Regression Assumptions
- Linear Regression in Python (Programming Tutorial)
- The Sigmoid Function and Binary Logistic Regression
- The Softmax Function and Multinomial Logistic Regression
- Logistic Regression in Python
Support Vector Machines & Kernel Methods
Unsupervised Machine Learning
For writing these posts I’ve relied on several textbooks, online courses, and blogs.