Coursera's Machine Learning

The programming assignments from Coursera's Machine Learning course taught by Andrew Ng.

Supervised learning algorithms：

• linear regression
• logistic regression
• neural networks
• SVM

For problems where you have labelled data and labelled examples like x(i), y(i).

Unsupervised learning:

• K-means clustering
• Principal Components Analysis for dimensionality reduction
• Anomaly Detection algorithms

When you have only unlabelled data x(i) Although Anomaly Detection can also use some labelled data to evaluate the algorithm.

Special applications:

• Recommender Systems
• large scale machine learning systems including parallelized and rapid-use systems
• sliding windows object classification for computer vision

Different aspects:

• bias and variance
• regularization help with some variance problems

Advice on building a machine learning system. And this involved both trying to understand what is it that makes a machine learning algorithm work or not work.

How to decide what to work on next && Debugging learning algorithms:

• evaluation of learning algorithms, evaluation metrics like precision recall, F1 score.
• practical aspects of evaluation like the training, cross-validation and test sets.
• diagnostics like learning curves.
• error analysis and ceiling analysis.

This is a wonderful course, thanks coursera and all the people worked for this course !
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