#!/usr/bin/env python # coding: utf-8 # ![logo](./Images/logo.png) #
# **Tutorials, examples, collections, and everything else that falls into the categories: pattern classification, machine learning, and data mining.** #
# # Table of Contents # - [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) # - [Statistical Pattern Classification Examples](#Statistical-Pattern-Classification-Examples) # - [Clustering](#Clustering) # - [Collecting Data](#Collecting-Data) # - [Resources](#Resources) # # Introduction to Machine Learning and Pattern Classification # * 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]](machine_learning/scikit-learn/python_data_entry_point.ipynb) # * An Introduction to simple linear supervised classification using `scikit-learn` [[IPython nb]](machine_learning/scikit-learn/scikit_linear_classification.ipynb) # # Pre-Processing # * **Feature Extraction** # * Tips and Tricks for Encoding Categorical Features in Classification Tasks [[IPython nb]](preprocessing/feature_encoding.ipynb) # * **Scaling and Normalization** # * About Feature Scaling: Standardization and Min-Max-Scaling (Normalization) [[IPython nb]](preprocessing/about_standardization_normalization.ipynb) # * **Feature Selection** # * Sequential Feature Selection Algorithms [[IPython nb]](dimensionality_reduction/feature_selection/sequential_selection_algorithms.ipynb) # * **Dimensionality Reduction** # * Principal Component Analysis (PCA) [[IPython nb]](dimensionality_reduction/projection/principal_component_analysis.ipynb) # * PCA based on the covariance vs. correlation matrix [[IPython nb]](dimensionality_reduction/projection/pca_cov_cor.ipynb) # * Linear Discriminant Analysis (LDA) [[IPython nb]](dimensionality_reduction/projection/linear_discriminant_analysis.ipynb) # * The effect of scaling and mean centering of variables prior to a PCA [[PDF]](./dimensionality_reduction/projection/scale_center_pca/scale_center_pca.pdf) # * Kernel tricks and nonlinear dimensionality reduction via PCA [[IPython nb]](dimensionality_reduction/projection/kernel_pca.ipynb) # * **Representing Text** # * Tf-idf Walkthrough for scikit-learn [[IPython nb](./machine_learning/scikit-learn/tfidf_scikit-learn.ipynb)] # # Model Evaluation # * An Overview of General Performance Metrics of Binary Classifier Systems [[PDF](./evaluation/performance_metrics/performance_metrics.pdf)] # * **Cross-Validation** # * Streamline your cross-validation workflow - scikit-learn's Pipeline in action [[IPython nb]](machine_learning/scikit-learn/scikit-pipeline.ipynb) # * Model evaluation, model selection, and algorithm selection in machine learning - Part I [[Markdown]](./evaluation/model-evaluation/model-evaluation-selection-part1.md) # * Model evaluation, model selection, and algorithm selection in machine learning - Part II [[Markdown]](./evaluation/model-evaluation/model-evaluation-selection-part2.md) # # Parameter Estimation # * **Parametric Techniques** # * Introduction to the Maximum Likelihood Estimate (MLE) [[IPython nb]](parameter_estimation_techniques/maximum_likelihood_estimate.ipynb) # * How to calculate Maximum Likelihood Estimates (MLE) for different distributions [[IPython nb]](parameter_estimation_techniques/max_likelihood_est_distributions.ipynb) # # * **Non-Parametric Techniques** # * Kernel density estimation via the Parzen-window technique [[IPython nb]](parameter_estimation_techniques/parzen_window_technique.ipynb) # * The K-Nearest Neighbor (KNN) technique # # * **Regression Analysis** # * Linear Regression # * Least-Squares fit [[IPython nb]](data_fitting/regression/linregr_least_squares_fit.ipynb) # * Non-Linear Regression # # Machine Learning Algorithms # #### Bayes Classification # # - Naive Bayes and Text Classification I - Introduction and Theory [[View PDF](http://sebastianraschka.com/PDFs/articles/naive_bayes_1.pdf)] [[Download PDF](./machine_learning/naive_bayes_1/tex/naive_bayes_1.pdf)] # # #### Logistic Regression # # - Out-of-core Learning and Model Persistence using scikit-learn # [[IPython nb](./machine_learning/scikit-learn/outofcore_modelpersistence.ipynb)] # # #### Neural Networks # # - Artificial Neurons and Single-Layer Neural Networks - How Machine Learning Algorithms Work Part 1 [[IPython nb](./machine_learning/singlelayer_neural_networks/singlelayer_neural_networks.ipynb)] # # - Activation Function Cheatsheet [[IPython nb](./machine_learning/neural_networks/ipynb/activation_functions.ipynb)] # # #### Ensemble Methods # # - Implementing a Weighted Majority Rule Ensemble Classifier in scikit-learn [[IPython nb](./machine_learning/scikit-learn/ensemble_classifier.ipynb)] # # #### Decision Trees # # - Cheatsheet for Decision Tree Classification [[IPython nb]('./machine_learning/decision_trees/decision-tree-cheatsheet.ipynb')] # # Clustering # - **Protoype-based clustering** # - **Hierarchical clustering** # - Complete-Linkage Clustering and Heatmaps in Python [[IPython nb](./clustering/hierarchical/clust_complete_linkage.ipynb)] # - **Density-based clustering** # - **Graph-based clustering** # - **Probabilistic-based clustering** # # Collecting Data # - Collecting Fantasy Soccer Data with Python and Beautiful Soup [[IPython nb](./data_collecting/parse_dreamteamfc_data.ipynb)] # # - Download Your Twitter Timeline and Turn into a Word Cloud Using Python [[IPython nb](./data_collecting/twitter_wordcloud.ipynb)] # # - Reading MNIST into NumPy arrays [[IPython nb](./data_collecting/reading_mnist.ipynb)] # # Statistical Pattern Classification Examples # * **Supervised Learning** # * Parametric Techniques # * Univariate Normal Density # * Ex1: 2-classes, equal variances, equal priors [[IPython nb]](stat_pattern_class/supervised/parametric/1_stat_superv_parametric.ipynb) # * Ex2: 2-classes, different variances, equal priors [[IPython nb]](stat_pattern_class/supervised/parametric/2_stat_superv_parametric.ipynb) # * Ex3: 2-classes, equal variances, different priors [[IPython nb]](stat_pattern_class/supervised/parametric/3_stat_superv_parametric.ipynb) # * Ex4: 2-classes, different variances, different priors, loss function [[IPython nb]](stat_pattern_class/supervised/parametric/4_stat_superv_parametric.ipynb) # * Ex5: 2-classes, different variances, equal priors, loss function, cauchy distr.[[IPython nb]](stat_pattern_class/supervised/parametric/5_stat_superv_parametric.ipynb) # # * Multivariate Normal Density # * Ex5: 2-classes, different variances, equal priors, loss function [[IPython nb]](stat_pattern_class/supervised/parametric/5_stat_superv_parametric.ipynb) # * Ex7: 2-classes, equal variances, equal priors [[IPython nb]](stat_pattern_class/supervised/parametric/7_stat_superv_parametric.ipynb) # # * Non-Parametric Techniques # # Resources # * Matplotlib examples - Visualization techniques for exploratory data analysis [[IPython nb]](resources/matplotlib_viz_gallery.ipynb) # # * Copy-and-paste ready LaTex equations [[Markdown]](resources/latex_equations.md) # # * Open-source datasets [[Markdown]](resources/dataset_collections.md) # # * Free Machine Learning eBooks [[Markdown]](resources/machine_learning_ebooks.md) # # * Terms in data science defined in less than 50 words [[Markdown]](resources/data_glossary.md) # # * Useful libraries for data science in Python [[Markdown]](resources/python_data_libraries.md) # # * General Tips and Advices [[Markdown]](resources/general_tips_and_advices.md) # # * A matrix cheatsheat for Python, R, Julia, and MATLAB [[HTML]](http://sebastianraschka.com/github/pattern_classification/matrix_cheatsheet_table.html)