Basics for Machine Learning
This repository contains python implementations of some of the fundamental Machine Learning models and algorithms based off of Erik Linder-Noren's post.
Note, the purpose of this is not to produce as optimized and computationally efficient algorithms as possible, but rather to present the inner workings of them in a clear and accessible way.
Added in a Study Guide about basic statistcs using point estimators, confidence intervals, and such. More Examples
Added in are the study guide and intuition behind Support Vector. There are a lot of websites I used to help fully understand SVM as well as its implementation.
Also added in example scripts using the sklearn libraries
Introduction to Support Vector Machines (https://www.ritchieng.com/machine-learning-svms-support-vector-machines/)
How to derive the cost function from Logisitc Regression
Regularization in Logisitc Regression
Formal Introduction to SVM by Andrew Ng
The Support Vector Machine and regularization MIT Notes
Questions and Answers: Imperative for SVM
How do you know which method of Cross Validation is best?
Cross-Validation in Machine Learning
Introduction to Gradient Descent Algorithm (along with variants) in Machine Learning
Paper: An overview of gradient descent optimization algorithms
A comprehensive beginners guide for Linear Regression, Ridge and Lasso Regularization
Regularization in Machine Learning
Least-squares fitting using matrix derivatives
Added in lots of resource for learning Machine Learning
Dimensionality Reduction Algorithms: Strengths and Weaknesses
Python Quickstart Guide for Data Science
9 Mistakes to Avoid When Starting Career in Data Science
The Hitchhiker’s Guide to Machine Learning in Python
Python Machine Learning: Scikit-Learn Tutorial
Learning Python for Machine Learning Tutorial -- follow the links below
Thank you Erik Linder-Noren for your intuition, code, and findings.