A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks
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README.md
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README.md

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**A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks.**



Machine Learning and Pattern Classification








Machine learning and pattern classification with scikit-learn

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  • Entry Point: Data - Using Python's sci-packages to prepare data for Machine Learning tasks and other data analyses [IPython nb]

  • An Introduction to simple linear supervised classification using scikit-learn [IPython nb]






Pre-processing

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  • About Feature Scaling: Standardization and Min-Max-Scaling (Normalization) [IPython nb]







Techniques for Dimensionality Reduction

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  • Projection

    • Component Analyses
      • Linear Transformation
  • Feature Selection

    • Sequential Feature Selection Algorithms [IPython nb]



Techniques for Parameter Estimation

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  • Parametric Techniques

    • Introduction to the Maximum Likelihood Estimate (MLE) [IPython nb]
    • How to calculate Maximum Likelihood Estimates (MLE) for different distributions [IPython nb]
  • Non-Parametric Techniques

    • Kernel density estimation via the Parzen-window technique [IPython nb]
    • The K-Nearest Neighbor (KNN) technique
  • Regression Analysis

    • Linear Regression

    • Non-Linear Regression




Statistical Pattern Recognition Examples

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  • Supervised Learning

    • Parametric Techniques

      • Univariate Normal Density

        • Ex1: 2-classes, equal variances, equal priors [IPython nb]
        • Ex2: 2-classes, different variances, equal priors [IPython nb]
        • Ex3: 2-classes, equal variances, different priors [IPython nb]
        • Ex4: 2-classes, different variances, different priors, loss function [IPython nb]
        • Ex5: 2-classes, different variances, equal priors, loss function, cauchy distr. [IPython nb]
      • Multivariate Normal Density

        • Ex5: 2-classes, different variances, equal priors, loss function [IPython nb]
        • Ex7: 2-classes, equal variances, equal priors [IPython nb]
    • Non-Parametric Techniques

  • Unsupervised Learning




Links to useful resources

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Dataset Collections

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  • Kaggle - Kaggle, the leading platform for predictive modeling competitions.

  • UCI MLR - UC Irvine Machine Learning Repository

  • google.com/publicdata - public data maintained by Google

  • Freebase - A community-curated database of well-known people, places, and things

  • mldata.org - machine learning data set repository for uploading and finding data sets

  • Infochimps - a huge collection of large-sized data sets



Specialized Datasets

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