Overview

There are some notes and codes made by myself in this project, "MOOCsLearning". These sources are made from some courses provided by Coursera, Udemy or Microsoft. By the way, move your mouse, let the page down, you can see the links of my notes and code

From Coursera

• Machine Learning (by Andrew Ng)
• Algorithm, Part I (by Kevin Wayne and Robert Sedgewick)
• Python Data Structure (by Charles Severance)
• Using Python to Access Web Data (by Charles Severance)
• Mathematics for Machine Learning : Linear Algebra (by David Dye, Samuel J. Cooper, and A Freddie Page)

From Udemy

• 建網百科全書 (by Ken)
• Python 與資料科學入門 (by 郭耀仁)

From Microsoft

• Data Science Orientation (by Graeme Malcolm and Liberty J. Munson)

Machine Learning

The machine learning course was made by a well-known Stanford professor, Andrew Ng. There are three million students having enrolled it on Coursera platform. Many people recommend this course for ML beginners

Code

Code01 : Linear Regression with Multiple Variables
Code02 : Logistic Regression and Regularization
Code03 : Multi-class Classification and Neural Networks
Code04 : Neural Network Learning
Code05 : Advice for Applying ML and ML System Design
Code06 : Support Vector Machines
Code07 : K-Means Clustering and PCA
Code08 : Anomaly Detection and Recommender Systems

Note

Note01 : Introduction of ML and Linear Regression With One Variable
Note02 : Linear Regression with Multiple Variables
Note03 : Logistic Regression and Regularization
Note04 : Multi-class Classification and Neural Networks
Note05 : Neural Network Learning
Note06 : Advice for Applying ML and ML System Design
Note07 : Support Vector Machines
Note08 : K-Means Clustering and PCA
Note09 : Anomaly Detection and Recommender Systems
Note10 : Large Scale Machine Learning
Note11 : Application Example Photo OCR

Algorithm, Part I

Programming Assignment

Assignment01：Percolation
Assignment02：
Assignment03：

Programming Practice

Practice01：Quick-find to solve dynamic connectivity
Practice02：
Practice03：

Python Data Structure

Code

Code01：Strings
Code02：Files
Code03：Lists
Code04：Dictionaries
Code05：Tuples

Note

Python data structure

Using Python to Access Web Data

Code

Code01：Extracting Data With Regular Expressions
Code02：Understanding the Request Response Cycle
Code03：Scraping HTML Data with BeautifulSoup
Code04：Following Links in HTML Using BeautifulSoup
Code05：Extracting Data from XML
Code06：Extracting Data from JSON
Code07：Using the GeoJSON API

Note

Note01：Regular Expressions
Note02：Network and Sockets
Note03：Programs that Surf the Web
Note04：Web Services and XML
Note05：JSON and the REST Architecture

Mathematics for Machine Learning : Linear Algebra

Note

Mathematics for Machine Learning : Linear Algebra

建網百科全書

Project

Project01：HTML
Project02：CSS

Code

Code01：HTML(Form)
Code02：HTML(Others)

Note

Note01：HTML
Note02：CSS

Python 與資料科學入門

Note

Python 與 資料科學入門

Data Science Orientation

Note

資料科學概論