ml_concepts

A collection of Jupyter notebooks walking through various machine learning concepts, including:

• Generalized Linear Models
• Optimization
  • Maximum Likelihood Estimation
  • Gradient descent
• Ordinary Least Squares
• K-nearest neighbors algorithm
• p-values, and why they're uniformly distributed under the null hypothesis
• a simple Python implementation of a neural network without using any deep learning libraries
• resampling methods