Tutorials, examples, collections, and everything else that falls into the categories: pattern classification, machine learning, and data mining

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**Tutorials, examples, collections, and everything else that falls into the categories: pattern classification, machine learning, and data mining.**



Sections



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Introduction to Machine Learning and Pattern Classification

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  • 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]





Pre-processing

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  • 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]
    • The effect of scaling and mean centering of variables prior to a PCA [PDF] [HTML]
    • PCA based on the covariance vs. correlation matrix [IPython nb]
    • Linear Discriminant Analysis (LDA) [IPython nb]
      • Kernel tricks and nonlinear dimensionality reduction via PCA [IPython nb]

  • Representing Text

    • Tf-idf Walkthrough for scikit-learn [IPython nb]


Model Evaluation

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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]
  • 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]


Parameter Estimation

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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]

  • Non-Parametric Techniques

    • Kernel density estimation via the Parzen-window technique [IPython nb]
    • The K-Nearest Neighbor (KNN) technique

  • Regression Analysis

    • Linear Regression

    • Non-Linear Regression



Machine Learning Algorithms

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Bayes Classification

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

Logistic Regression

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

Neural Networks

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

  • Activation Function Cheatsheet [IPython nb]

Ensemble Methods

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

Decision Trees

  • Cheatsheet for Decision Tree Classification [IPython nb]


Clustering

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  • Protoype-based clustering
  • Hierarchical clustering
    • Complete-Linkage Clustering and Heatmaps in Python [IPython nb]
  • Density-based clustering
  • Graph-based clustering
  • Probabilistic-based clustering


Collecting Data

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  • 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]



Data Visualization

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  • Exploratory Analysis of the Star Wars API [IPython nb]

  • Matplotlib examples -Exploratory data analysis of the Iris dataset [IPython nb]

  • Artificial Intelligence publications per country

[IPython nb] [PDF]



Statistical Pattern Classification Examples

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  • 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



Books

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Python Machine Learning



Talks

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An Introduction to Supervised Machine Learning and Pattern Classification: The Big Picture

[View on SlideShare]

[Download PDF]



MusicMood - Machine Learning in Automatic Music Mood Prediction Based on Song Lyrics

[View on SlideShare]

[Download PDF]



Applications

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MusicMood - Machine Learning in Automatic Music Mood Prediction Based on Song Lyrics

This project is about building a music recommendation system for users who want to listen to happy songs. Such a system can not only be used to brighten up one's mood on a rainy weekend; especially in hospitals, other medical clinics, or public locations such as restaurants, the MusicMood classifier could be used to spread positive mood among people.

[musicmood GitHub Repository]


mlxtend - A library of extension and helper modules for Python's data analysis and machine learning libraries.

[mlxtend GitHub Repository]



Resources

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  • 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]

Comments
  • creating new README notebook with local links

    creating new README notebook with local links

    Hello,

    I'd like to use these excellent notes for my research lab. However, we have a clone-and-work-locally type of workflow and it would be easiest for them if everything was local (no links to github), so that they can add, amend notes etc as we go through it together.

    I created a new README notebook with a table of contents and local links. This involved me copying a file from another of your repositories.

    However, Im not sure about how to deal with the markdown files. If users dont have a markdown renderer installed, the browser opens the raw file. Possible solutions:

    • keep links to github version
      • then they cant add/edit/amend their local notes
    • have them install a markdown renderer?
    • convert the markdown files to complete ipython notebooks so that they can easily add/edit
      • my preferred solution, but I just wanted to see your opinion before I do the work and submit another pull request?
    opened by diego898 6
  • Correct min/max typo

    Correct min/max typo

    Hi Sebastian,

    Thank you for this set of articles. They're awesome. I found this one minor typo as I was working through the feature scaling article. Also, on your blog itself (but not in this iPython notebook), there seems to be some duplication. From 'The effect of standardization on PCA in a pattern classification task' onward, the content appears twice.

    That is on this page: http://www.sebastianraschka.com/Articles/2014_about_feature_scaling.html#The-effect-of-standardization

    opened by clebio 3
  • Update introduction_to_supervised_machine_learning.md

    Update introduction_to_supervised_machine_learning.md

    I believe "barcode scanners in the supermarket" is not a pattern classification such that we can consider it as a machine learning problem. It is just a way we encode digits. So, I think an example about "article recommendation" makes more sense.

    opened by dpfaq 1
  • Update scikit_linear_classification and add missing legend

    Update scikit_linear_classification and add missing legend

    One of the plots defines labels, but did not use them in a legend. This has been added.

    Also, while reviewing the notebook, I came across some DeprecationWarnings from sklearn, and have updated the code accordingly.

    opened by proinsias 1
  • Problem with the Heatmap plus Row-dendrogram plot

    Problem with the Heatmap plus Row-dendrogram plot

    There's a problem with the Heatmap plus Row-dendrogram plot in the Hierarchical Agglomerative Clustering - Complete Linkage Clustering notebook. The dendrogram is actually up-side-down, I noticed this when I was using your example as a guide. My fix was to reverse the index of the data and the index labels, I could not find a clever way of reversing the dendrogram. Thanks for a great guide anyway. selection_002

    opened by freko247 1
  • Clearly mark libraries depending on a web service

    Clearly mark libraries depending on a web service

    Libraries depending on a web service should be clearly marked as such. Example: plot.ly I'd expect a library to be usable without a proprietary web service, else it's really an API only.

    opened by mkesper 1
  • upd naive-bayes-classifier

    upd naive-bayes-classifier

    Notes from Data Mining Class (Tan) i.i.d. is the assumption made in Bayes theorem, which states that under iid posterior probability is obtained from likelihood * prior / evidence Naive Bayes assumes an extra conditional independence, which makes it much simpler

    opened by vmirly 0
  • Add

    Add "Launch in Deepnote" button

    Hi Sebastian, I'm Dan from Deepnote. We're a small startup creating a better data science notebook compatible with Jupyter and one of the things we're really interested is making it easier for people to try and learn from existing projects. I was trying out Deepnote with some interesting public repos, including your and since I already set it up for myself, I thought it might be useful for others too to have a one-click button to run your repo, so I submitted a PR. Hope you find it useful!

    Thanks for your work, Dan

    opened by danzvara 3
Owner
Sebastian Raschka
Machine Learning researcher & passionate open source contributor. Author of the "Python Machine Learning" book.
Sebastian Raschka
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