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

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Table of Contents¶

• Introduction to Machine Learning and Pattern Classification
• Pre-Processing
• Model Evaluation
• Parameter Estimation
• Machine Learning Algorithms
  • Bayes Classification
  • Logistic Regression
  • Neural Networks
  • Ensemble Methods
• Statistical Pattern Classification Examples
• Clustering
• Collecting Data
• Resources

Introduction to Machine Learning and Pattern Classification¶

• 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]

Pre-Processing¶

• Feature Extraction
  • Tips and Tricks for Encoding Categorical Features in Classification Tasks [IPython nb]
• 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]
  • PCA based on the covariance vs. correlation matrix [IPython nb]
  • Linear Discriminant Analysis (LDA) [IPython nb]
  • The effect of scaling and mean centering of variables prior to a PCA [PDF]
  • Kernel tricks and nonlinear dimensionality reduction via PCA [IPython nb]
• Representing Text
  • Tf-idf Walkthrough for scikit-learn [IPython nb]

Model Evaluation¶

• 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]
• Model evaluation, model selection, and algorithm selection in machine learning - Part I [Markdown]
• Model evaluation, model selection, and algorithm selection in machine learning - Part II [Markdown]

Parameter Estimation¶

• 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

Machine Learning Algorithms¶

Bayes Classification¶

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

Logistic Regression¶

• Out-of-core Learning and Model Persistence using scikit-learn [IPython nb]

Neural Networks¶

• Artificial Neurons and Single-Layer Neural Networks - How Machine Learning Algorithms Work Part 1 [IPython nb]

• Activation Function Cheatsheet [IPython nb]

Ensemble Methods¶

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

Decision Trees¶

• Cheatsheet for Decision Tree Classification [IPython nb]

Clustering¶

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

Collecting Data¶

• 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]

• Reading MNIST into NumPy arrays [IPython nb]

Statistical Pattern Classification Examples¶

• 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

Resources¶

• Matplotlib examples - Visualization techniques for exploratory data analysis [IPython nb]

• 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]