A Visual Introduction to Machine Learning

http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

“In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions.”

Principal Component Analysis is an example of a very low-level sort of machine learning algorithm. By identifying things that areĀ probably (within some statistical certainty) in a training dataset, you can identify the same things in a real-world dataset. This is why Google’s Deep Dream needs a training image — it needs to know what to look for.

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A Visual Introduction to Machine Learning

Principal Component Analysis Explained Visually

http://setosa.io/ev/principal-component-analysis/

“Principal component analysis (PCA) is a technique used to emphasize variation and bring out strong patterns in a dataset. It’s often used to make data easy to explore and visualize.”

PCA is very useful for pulling information out of large datasets, particularly multi-dimensional datasets, where correlations and relationships between different variables might otherwise have been difficult to see.

Principal Component Analysis Explained Visually