Learning Resources

Books

Understanding Machine Learning, From Theory to Algorithms. Shai Shalev-Shwartz and Shai Ben-David

Concepts in Machine Learning

Kernel Methods: http://kernel-methods.net
Support Vectors: http://support-vector.net
A collection of resources or SVM: http://svms.org
Everythong you Want to Know about Kernel Trick: http://www.eric-kim.net/eric-kim-net/posts/1/kernel_trick.html

Data Clustering

Vector Quantization: http://www.data-compression.com/vq.shtml
Mean-shift clustering: http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/TUZEL1/MeanShift.pdf
Affinity Propagation, Clustering By Passing Messages: http://www.cs.columbia.edu/~delbert/docs/DDueck-thesis_small.pdf

Text Analysis

Topic modeling: gensim https://radimrehurek.com/gensim

Time-series data

Fourier Transformation http://www.thefouriertransform.com

Hadoop (Java)

Customizing mapper and reducer: http://hadooptutorial.info/creating-custom-hadoop-writable-data-type/

Tools and Softwares

Building Hidden Markov Models in Python: http://hmmlearn.readthedocs.org/en/latest

Datasets

MNIST (handwritten digits) dataset: http://yann.lecun.com/exdb/mnist/
Movie Dataset: http://grouplens.org/datasets/movielens/
Million Song Dataset: http://labrosa.ee.columbia.edu/millionsong/
Adult dataset: http://archive.ics.uci.edu/ml/datasets/Adult
Star Cluster: (Hertzsprung-Russell Diagram Data of Star Cluster CYG OB1) https://vincentarelbundock.github.io/Rdatasets/doc/robustbase/starsCYG.html