mlcourse_open - OpenDataScience Machine Learning course. Both in English and Russian

•    Python

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This is the list of published articles on medium.com 🇬🇧, habr.com 🇷🇺, and jqr.com 🇨🇳. Icons are clickable. Also, links to Kaggle Kernels (in English) are given. This way one can reproduce everything without installing a single package. Assignments will be announced each week. Meanwhile, you can pratice with demo versions. Solutions will be discussed in the upcoming run of the course.

machine-learning data-analysis data-science pandas algorithms numpy scipy matplotlib seaborn plotly scikit-learn kaggle-inclass vowpal-wabbit ipynb docker math

devml - Machine Learning, Statistics and Utilities around Developer Productivity, Company Productivity and Project Productivity

•    Jupyter

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This pip install installs a command-line tool: dml (which is referenced in the documentation below). And also library devml, which is referenced below as well. Code is written to support Python 3.6 or greater. You can get that here: https://www.python.org/downloads/release/python-360/.

pandas github productivity machine-learning seaborn jupyter-notebook git churn-statistics defects data-science visualization machine-intelligence ai

intro-to-machine-learning - Introduction to Machine Learning

•    Jupyter

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The code in this repository is inspired by Scikit Learn and From Data With Love. You can also run pip install -r requirements.txt to install all required pacakges..

scikit-learn machine-learning pandas seaborn

cshl-singlecell-2017 - Single Cell Analysis course at Cold Spring Harbor Laboratory 2017

•    Jupyter

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This is one of many single cell courses/tutorials. An excellent list of all single cell package, courses, tutorials, speakers for conferences, can be found here. We'll use some additional dependencies outside of the scientific python ecosystem.

singlecell single-cell bioinformatics machinelearning pca tsne scikit-learn matplotlib seaborn jupyter-notebook

Data-Science-for-Marketing-Analytics - Achieve your marketing goals with the data analytics power of Python

•    Jupyter

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Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of it based on the segments. The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices.

data-visualization data-science numpy pandas matplotlib seaborn

Data-Visualization-with-Python - Discover how Matplotlib and Seaborn can help clearly communicate and present your newly acquired insight

•    Jupyter

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Discover how Matplotlib and Seaborn can help clearly communicate and present your newly acquired insight

data-visualization seaborn matplotlib geospatial-data plot visualization pandas numpy bokeh

machine-learning-alpine - Alpine Container for Machine Learning

•    Jupyter

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See the Welcome.ipynb to see the package versions.

machine-learning xgboost scikit-learn pandas numpy seaborn matplotlib docker alpine jupyter-notebook tensorflow keras

dash-alternative-viz - Dash components & demos to create Altair, Matplotlib, Highcharts , and Bokeh graphs within Dash apps

•    Javascript

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In Dash's built-in dash_core_components library, the dcc.Graph component uses standard Plotly figures. Dash’s component plugin system provides a toolchain to create Dash components from any JavaScript-based library. dash-alternative-viz is a proof-of-concept Dash component library that provides Dash interfaces to Altair, matplotlib (or any compatible system like Seaborn, Pandas.plot, Plotnine and others!), Bokeh (with or without HoloViews), and HighCharts.

highcharts seaborn vega vega-lite bokeh matplotlib altair holoviews plotly-dash

starborn - Starborn: Seaborn-compatible data visualization for Python based on Altair

•    Python

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Starborn is a Python visualization library based on Vega and Altair that aims to be API-compatible with Seaborn. Like Seaborn, it provides a high-level interface for drawing attractive statistical graphics. Thanks to the underlying libraries, it can also offer interactivity with in-browser panning and zooming. Development status: Alpha.

visualization charts jupyter pandas seaborn