- 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]

**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]

- 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]

**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

- Linear Regression

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

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

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

Activation Function Cheatsheet [IPython nb]

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

- Cheatsheet for Decision Tree Classification [IPython nb]

**Protoype-based clustering****Hierarchical clustering**- Complete-Linkage Clustering and Heatmaps in Python [IPython nb]

**Density-based clustering****Graph-based clustering****Probabilistic-based clustering**

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]

**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

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]