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
- anomaly detection with Google Analytics (example)


Must purchase this article (I did not purchase but appears to be good)

- Gaussian distribution


XVI) Recommender Systems

- Collaborative Filtering

XVII) Large Scale Machine Learning


- stochastic gradient descent

- parallelized stochastic gradient descent


- recursive partitioning:


Machine Learning 201:


Online Lectures:


Deep Learning:


Sparse Coding:

Some good articles on working with the command line:


Jacobian Iteration for Singular Value Decomposition:




Mathematics, Statistical Theory and Probability Theory:


Methods of Optimization:


Theoretical Computer Science:


Random but Important Things:






Miscellaneous Links:


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