**Tutorials, examples, collections, and everything else that falls into the categories: pattern classification, machine learning, and data mining.**
- Introduction to Machine Learning and Pattern Classification
- Pre-Processing
- Model Evaluation
- Parameter Estimation
- Machine Learning Algorithms and Classification Models
- Clustering
- Statistical Pattern Classification Examples
- Resources
[Download a PDF version] of this flowchart.
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Predictive modeling, supervised machine learning, and pattern classification - the big picture [Markdown]
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Entry Point: Data - Using Python's sci-packages to prepare data for Machine Learning tasks and other data analyses [IPython nb]
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An Introduction to simple linear supervised classification using
scikit-learn
[IPython nb]
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Scaling and Normalization
- About Feature Scaling: Standardization and Min-Max-Scaling (Normalization) [IPython nb]
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Feature Selection
- Sequential Feature Selection Algorithms [IPython nb]
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Dimensionality Reduction
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Principal Component Analysis (PCA) [IPython nb]
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The effect of scaling and mean centering of variables prior to a PCA [PDF] [HTML]
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PCA based on the covariance vs. correlation matrix [IPython nb]
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Linear Discriminant Analysis (LDA) [IPython nb]
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Kernel tricks and nonlinear dimensionality reduction via PCA [IPython nb]
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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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Parametric Techniques
- Introduction to the Maximum Likelihood Estimate (MLE) [IPython nb]
- How to calculate Maximum Likelihood Estimates (MLE) for different distributions [IPython nb]
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Non-Parametric Techniques
- Kernel density estimation via the Parzen-window technique [IPython nb]
- The K-Nearest Neighbor (KNN) technique
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Regression Analysis
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Linear Regression
- Least-Squares fit [IPython nb]
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Non-Linear Regression
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- Naive Bayes and Text Classification I - Introduction and Theory [View PDF] [Download PDF]
- 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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Supervised Learning
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Parametric Techniques
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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]
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Multivariate Normal Density
- Ex5: 2-classes, different variances, equal priors, loss function [IPython nb]
- Ex7: 2-classes, equal variances, equal priors [IPython nb]
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Non-Parametric Techniques
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Matplotlib examples - Visualization techniques for exploratory data analysis [IPython nb]
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Copy-and-paste ready LaTex equations [Markdown]
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Open-source datasets [Markdown]
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Free Machine Learning eBooks [Markdown]
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Terms in data science defined in less than 50 words [Markdown]
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Useful libraries for data science in Python [Markdown]
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General Tips and Advices [Markdown]
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A matrix cheatsheat for Python, R, Julia, and MATLAB [HTML]