Introduction Machine Learning: K-nearest neighbors and Perceptron Learning Algorithm

Machine learning is a subfield of computer science where computers have the ability to learn without being explicitly programmed. List of the machine learning task:

  • Supervised: Approximation where the all data is labeled and the algorithms learn to predict the output from the input data (training, cross validation and testing sets). We have two types of supervised problems:
    • Regression: When the output variable is a real value, such as “dollars” or “age”.
    • Classification: When the output variable is a category, such as “cat” or “dog” or “Tumor benign” and “Tumor malignant”.

screen-shot-2017-03-05-at-2-08-54-pmRegression vs Classification

  • Unsupervised: Description where the all data is unlabeled and the algorithms learn to inherent structure from the input data.
    • Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior.
    • Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y.

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