README.ipynb

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