In order to show how I understand statistical theories and machine learning argolithms, I applied knowledge to analyse some data sets and also coded some argolithms from textbooks.

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##Data analysis with Spark Data analysis and engineering at PySpark was performed

Click-through rate(CTR) prediction

Movie Recommendation with MLlib

Building a word count application

##Machine learning algorighms In order to understand theoritically and be able to use knowledge practically, algorithms were coded from scratch with Python(Pandas, Scipy, Numpy) and figures on the textbook are reproduced from understanding of equations. Algorithms comes from following book. The numbering indicate the chapter number of the book.

Christopher Bishop. (2007). Pattern Recognition and Machine Learning (Information Science and Statistics). Springer

1.1 Polynomial Curve Fitting

1.2 Bayesian curve fitting

2.5 Nonparametoric methods

3.3 Bayesian Linear Regression

4.1.7 The perceptron algorithm

5.1 Neural Network

7.1 Sparse kernel machine

##Statistical Analysis Statistical analysis was conducted with R package and sample data.

Generalized linear mixed model

Comparison GLM vs Bayesian modeling

State space model with MCMC

##Algorithm coding practiec in Python Problems provided by CodingBat(http://codingbat.com/python) were solved.

Warmup-1

Warmup-2

String-1

String-2

Logic-1

Logic-2

List-1

List-2