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<hr> **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
Clustering
Collecting Data
Data Visualization
Statistical Pattern Classification Examples
Books
Talks
Applications
Resources

[Download a PDF version] of this flowchart.

<hr>

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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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]
- 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]
Representing Text
- Tf-idf Walkthrough for scikit-learn [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]
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]

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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

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Bayes Classification

Naive Bayes and Text Classification I - Introduction and Theory [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]

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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]
Reading MNIST into NumPy arrays [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]

Artificial Intelligence publications per country

[IPython nb] [PDF]

<hr>

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

<hr>

Books

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Python Machine Learning

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Talks

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An Introduction to Supervised Machine Learning and Pattern Classification: The Big Picture

[View on SlideShare]

[Download PDF]

MusicMood - Machine Learning in Automatic Music Mood Prediction Based on Song Lyrics

[View on SlideShare]

[Download PDF]

<hr>

Applications

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MusicMood - Machine Learning in Automatic Music Mood Prediction Based on Song Lyrics

This project is about building a music recommendation system for users who want to listen to happy songs. Such a system can not only be used to brighten up one's mood on a rainy weekend; especially in hospitals, other medical clinics, or public locations such as restaurants, the MusicMood classifier could be used to spread positive mood among people.

[musicmood GitHub Repository]

mlxtend - A library of extension and helper modules for Python's data analysis and machine learning libraries.

[mlxtend GitHub Repository]

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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]