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

Sections

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]

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]
- Linear Discriminant Analysis (LDA) [IPython nb]
- Kernel tricks and nonlinear dimensionality reduction via PCA [IPython nb]

Cross-validation
- Streamline your cross-validation workflow - scikit-learn's Pipeline in action [IPython nb]

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

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]