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A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks
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 <hr> **Tutorials, examples, collections, and everything else that falls into the categories: pattern classification, machine learning, and data mining.** <br> <br> <br> <br> # Sections - [Introduction to Machine Learning and Pattern Classification](#introduction-to-machine-learning-and-pattern-classification) - [Pre-Processing](#pre-processing) - [Model Evaluation](#model-evaluation) - [Parameter Estimation](#parameter-estimation) - [Machine Learning Algorithms](#machine-learning-algorithms) - [Bayes Classification](#bayes-classification) - [Logistic Regression](#logistic-regression) - [Neural Networks](#neural-networks) - [Ensemble Methods](#ensemble-methods) - [Clustering](#clustering) - [Collecting Data](#collecting-data) - [Data Visualization](#data-visualization) - [Statistical Pattern Classification Examples](#statistical-pattern-classification-examples) - [Books](#books) - [Talks](#talks) - [Applications](#applications) - [Resources](#resources) <br> <br> <img src="./Images/supervised_learning_flowchart.png" style="width: 700px; height:600px;"> [[Download a PDF version](https://github.com/rasbt/pattern_classification/raw/master/PDFs/supervised_learning_flowchart.pdf)] of this flowchart. <br> <br> <br> <hr> <br> ### Introduction to Machine Learning and Pattern Classification [[back to top](#sections)] - Predictive modeling, supervised machine learning, and pattern classification - the big picture [[Markdown](./machine_learning/supervised_intro/introduction_to_supervised_machine_learning.md)] - Entry Point: Data - Using Python's sci-packages to prepare data for Machine Learning tasks and other data analyses [[IPython nb](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/machine_learning/scikit-learn/python_data_entry_point.ipynb)] - An Introduction to simple linear supervised classification using `scikit-learn` [[IPython nb](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/machine_learning/scikit-learn/scikit_linear_classification.ipynb)] <br> <br> <br> <hr> <br> ### Pre-processing [[back to top](#sections)] - **Feature Extraction** - Tips and Tricks for Encoding Categorical Features in Classification Tasks [[IPython nb](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/preprocessing/feature_encoding.ipynb)] - **Scaling and Normalization** - About Feature Scaling: Standardization and Min-Max-Scaling (Normalization) [[IPython nb](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/preprocessing/about_standardization_normalization.ipynb)] - **Feature Selection** - Sequential Feature Selection Algorithms [[IPython nb](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/dimensionality_reduction/feature_selection/sequential_selection_algorithms.ipynb)] - **Dimensionality Reduction** - Principal Component Analysis (PCA) [[IPython nb](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/dimensionality_reduction/projection/principal_component_analysis.ipynb)] - The effect of scaling and mean centering of variables prior to a PCA [[PDF](https://github.com/rasbt/pattern_classification/raw/master/dimensionality_reduction/projection/scale_center_pca/scale_center_pca.pdf)] [[HTML](http://htmlpreview.github.io/?https://raw.githubusercontent.com/rasbt/pattern_classification/master/dimensionality_reduction/projection/scale_center_pca/scale_center_pca.html)] - PCA based on the covariance vs. correlation matrix [[IPython nb](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/dimensionality_reduction/projection/pca_cov_cor.ipynb)] - Linear Discriminant Analysis (LDA) [[IPython nb](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/dimensionality_reduction/projection/linear_discriminant_analysis.ipynb)] - Kernel tricks and nonlinear dimensionality reduction via PCA [[IPython
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:bf1f2e133495d16e, llm:Repository topics: machine-learning, machine-learning-algorithms, pattern-classification; description and README: collection of tutorials, examples, and notebooks covering supervised learning, preprocessing, model evaluation, algorithms (Bayes, logistic regression, neural networks, ensembles), clustering, data visualization, etc.
matched fp:bf1f2e133495d16e, llm:Repository topics: machine-learning, machine-learning-algorithms, pattern-classification; description and README: collection of tutorials, examples, and notebooks covering supervised learning, preprocessing, model evaluation, algorithms (Bayes, logistic regression, neural networks, ensembles), clustering, data visualization, etc.
