This is my study notes from coursera - Machine Learning Courses by Washington University. It uses GraphLab (https://en.wikipedia.org/wiki/GraphLab), a graph-based, high performance, distributed computation framework written in C++, to perform machine learning tasks. GraphLab Create™ (developed by Dato) is a Python library that includes a simple Python interface on top of a fast C++ engine. The user guide can be found at https://dato.com/learn/userguide/

Two things you need to install: (1) iPython Notebook; (2) GraphLab Create

Note: The course is divided into two parts. Each week a new case study is investigated, and GraphLab functions are taught for each specific task. Only general concepts are explained, no nitty gritty algorithms for each ML model studied.

• Predict House Price - Regression
• Sentiment Analysis - Classification
• Document Retrieval - Clustering
• Product Recommendation - Matrix Factorization
• Visual Product Reccomender - Deep Learning

Note: This course focuses on implementing machine learning algorithms into regression models.

• Simple Linear Regression
• Multiple Regression
• Assess Performance - Bias-Variance Tradeoff
• Ridge Regression
• Lasso Regression
• k-Nearest-Neighbors Regression