Rehearsal of Machine Learning on Coursera

After years of working on traditional algorithm development on data structure, I recently changed to developing machine learning model-based algorithms. It’s time to rehearse the basics of machine learning, to adapt to my new job well.

Machine Learning on Coursera

The Machine Learning course on Coursera is developed by Andrew Ng, a professor at Stanford University. The course is designed to provide a broad introduction to machine learning, data-mining, and statistical pattern recognition. The course is divided into 11 weeks, each week has a set of video lectures, quizzes, and programming assignments. The course is free to audit, but you need to pay for the certificate.

It contains the following topics:

• Introduction
• Linear Regression with One Variable
• Linear Regression with Multiple Variables
• Octave/Matlab Tutorial
• Logistic Regression
• Regularization
• Neural Networks: Representation
• Neural Networks: Learning
• Advice for Applying Machine Learning
• Machine Learning System Design
• Support Vector Machines
• Unsupervised Learning
• Principal Component Analysis
• Anomaly Detection
• Recommender Systems
• Large Scale Machine Learning

Rehearsal

The rehearsal process is mainly based on practices, which are the programming assignments. The assignments are designed to help you understand the concepts and techniques taught in the course. The assignments are written in Octave/Matlab, which is a high-level language for numerical computations. The assignments are not difficult, but they are time-consuming. You need to spend a lot of time on them to understand the concepts and techniques.