harperd/machine-learning

Machine Learning Practice

AI and Machine Learning

Notes

Below are notes on various topics as I learn all things AI. These notes are updated as I learn more on the topic. My raw notes are a mixture of markdown and LaTeX so will need an editor such as Typora. Below are the same notes in PDF format.

Linear Regression

• Univariate Linear Regression
• Gradient Descent
• Pseudo Inverse
• Multivariate Linear Regression

Logistic Regression

• Logistic Regression
• Binary Logistic Regression
• Multiclass Logistic Regression
• Logistic Regression Vectorization

Neural Networks

• Introduction
• Types of Neural Networks
• Forward Propagation
• Classification and Cost
• Activation Functions
• Logic Gates
• Back Propagation
• Gradient Checking
• Parameter Initialization
• Unrolling Parameters
• Architecting a Basic Neural Network
• Training a Neural Network

Feature Engineering

• Feature Scaling
• Encoding
• Regularization

Other

• Important People in the Field of AI

Notebooks

As I study a topic I will create a notebook to apply concepts. Below are various notebooks on some of the topics above.

• Univariate Linear Regression
• Multivariate Linear Regression
• Binary Logistic Regression
• Multiclass Logistic Regression
• Neural Network
• K-Means Clustering
• K-Means Cluster Distance
• Data Visualization

Computational Thinking and Data Science

My notes from the MIT Introduction to Computational Thinking and Data Science course are here.

K-Means Clustering From Scratch

This was part of the MIT course and the full source can be found here.

Math Refreshers

• Linear Algebra - Vectors
• Linear Algebra - Matrices
• Calculus - Derivatives