jiqihumanr / pattern_classification

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

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**Tutorials, examples, collections, and everything else that falls into the categories: pattern classification, machine learning, and data mining.**

Sections

• Machine learning and pattern classification with scikit-learn
• Pre-Processing
• Techniques for Dimensionality Reduction
• Techniques for Parameter Estimation
• Statistical Pattern Recognition Examples
• Resources

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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]

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Pre-processing

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

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Techniques for Dimensionality Reduction

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• Projection

  • Component Analyses
    • Linear Transformation
      • Principal Component Analysis (PCA) [IPython nb]
      • Linear Discriminant Analysis (LDA) [IPython nb]

• Feature Selection

  • Sequential Feature Selection Algorithms [IPython nb]

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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

    • Least-Squares fit [IPython nb]

  • Non-Linear Regression

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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

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Resources

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• Copy-and-paste ready LaTex equations [Markdown]

• Open-source datasets [Markdown]

• Free Machine Learning eBooks [Markdown]

• Terms in data science defined in less than 50 words [Markdown]

• Useful libraries for data science in Python [Markdown]

• General Tips and Advices [Markdown]