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

Sections

• Introduction to Machine Learning and Pattern Classification
• Pre-Processing
• Model Evaluation
• Parameter Estimation
• Machine Learning Algorithms and Classification Models
• Clustering
• Collecting Data
• Data Visualization
• Statistical Pattern Classification Examples
• Resources

[Download a PDF version] of this flowchart.

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Introduction to Machine Learning and Pattern Classification

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• Predictive modeling, supervised machine learning, and pattern classification - the big picture [Markdown]

• 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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• Scaling and Normalization

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

• Feature Selection

  • Sequential Feature Selection Algorithms [IPython nb]

• Dimensionality Reduction

  • Principal Component Analysis (PCA) [IPython nb]

  • The effect of scaling and mean centering of variables prior to a PCA [PDF] [HTML]

  • PCA based on the covariance vs. correlation matrix [IPython nb]

  • Linear Discriminant Analysis (LDA) [IPython nb]

  • Kernel tricks and nonlinear dimensionality reduction via PCA [IPython nb]

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Model Evaluation

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• An Overview of General Performance Metrics of Binary Classifier Systems [PDF]
• Cross-validation
  • Streamline your cross-validation workflow - scikit-learn's Pipeline in action [IPython nb]

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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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Machine Learning Algorithms and Classification Models

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• Naive Bayes and Text Classification I - Introduction and Theory [View PDF] [Download PDF]

• Implementing a Weighted Majority Rule Ensemble Classifier in scikit-learn [IPython nb]

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Clustering

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• Protoype-based clustering
• Hierarchical clustering
  • Complete-Linkage Clustering and Heatmaps in Python [IPython nb]
• Density-based clustering
• Graph-based clustering
• Probabilistic-based clustering

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Collecting Data

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• Collecting Fantasy Soccer Data with Python and Beautiful Soup [IPython nb]

• Download Your Twitter Timeline and Turn into a Word Cloud Using Python [IPython nb]

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Data Visualization

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• Exploratory Analysis of the Star Wars API [IPython nb]

• Matplotlib examples -Exploratory data analysis of the Iris dataset [IPython nb]

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Statistical Pattern Classification 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

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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]

• A matrix cheatsheat for Python, R, Julia, and MATLAB [HTML]