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

  •    Python

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.

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

  •    Jupyter

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/.

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

  •    Jupyter

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..

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

  •    Jupyter

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.




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

  •    Jupyter

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.





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