Machine Learning Pathway

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:

Data Resources:


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