Machine Learning

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.

• How to Learn Machine Learning On Your Own

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

Linear Regression

• 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)

Logistic Regression

• The Sigmoid Function and Binary Logistic Regression
• The Softmax Function and Multinomial Logistic Regression
• Logistic Regression in Python

Support Vector Machines & Kernel Methods

• What is a Kernel?
• What is a Support Vector?
• Understanding Hinge Loss and the SVM Cost Function

Unsupervised Machine Learning

• Principal Components Analysis Explained for Dummies

Further Resources

For writing these posts I’ve relied on several textbooks, online courses, and blogs.

Here you find a comprehensive list of educational resources on machine learning and data science that I’ve used to write these posts and that I recommend for further study.