Note : I regularly update this list.

Machine Learning 101:

I. Introduction to Machine Learning

II.  Linear Regression

 

 

III) Linear Algebra

V) Linear Regression with Multiple Variables
- Gradient Descent

- Optimization

 

IV) Octave Tutorial

 

VI) Logistic Regression (LR)

VII) Regularization

overview using advanced math

 

VIII and IX) Neural Networks

- backpropagation

 

XI) Machine Learning System Design

 

Precision, recall, accuracy, …

 

XII) Support Vector Machines

 

XIII) Clustering

 

XIV) Dimensionality Reduction

 

XV) Anomaly Detection

 

- Google Analytics http://www.google.com/analytics/
- anomaly detection with Google Analytics (example)

 

Must purchase this article (I did not purchase but appears to be good) http://www.sciencedirect.com/science/article/pii/S138912860700062X

- Gaussian distribution

 

XVI) Recommender Systems

- Collaborative Filtering

XVII) Large Scale Machine Learning

 

- stochastic gradient descent

- parallelized stochastic gradient descent

 

- recursive partitioning:

 

Machine Learning 201:

 

Deep Learning:

 

Sparse Coding:


Some good articles on working with the command line:

 

Jacobian Iteration for Singular Value Decomposition:

 

Fortran:

 

Mathematics, Statistical Theory and Probability Theory:

 

Methods of Optimization:

 

Theoretical Computer Science:

 

Random but Important Things:

 

R:

 

Python:

 

Credits goes to Resources
I added some of my places to that list as well.