Scikit Libraries

Use any of the following services. Next, run the following commands: Finally, start Jupyter: I recommend Python 3.7. If you follow the installation instructions above, that's the version you will get…

What you can expect are 400 pages rich in useful material just about everything you need to know to get started with machine learning ... from theory to the actual code that you can directly put into…

TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data. Once TPOT is finished searching (or you get…

mlcourse.ai is still in self-paced mode but we offer you Bonus Assignments with solutions for a contribution of $17/month. The idea is that you pay for ~1-5 months while studying the course materials…

Install with pip You can install add-ons individually or all at once by running: Below is an example of using Deep Feature Synthesis (DFS) to perform automated feature engineering. In this example, w…

published September 20th, 2017 Oh, there are so many things that we improved or added; where should I start!? The one issue on top of my priority list was to fix all the nasty typos that were introdu…

Code repositories for the 1st and 2nd edition are available at to be published December 12th, 2019

Yellowbrick is compatible with Python 3.4 or later and also depends on scikit-learn and matplotlib. The simplest way to install Yellowbrick and its dependencies is from PyPI with pip, Python's prefer…

IPython Notebook(s) and other programming tools such as Torch/Lua/D lang in demonstrating deep learning functionality. Additional PyRo tutorials: IPython Notebook(s) demonstrating scikit-learn functi…

The ML workspace is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on you…

You can start with this article that I wrote in Heartbeat magazine (on Medium platform): Jupyter notebooks covering a wide range of functions and operations on the topics of NumPy, Pandans, Seaborn, …

A trove of carefully curated resources and links (on the topics of software, platforms, language, techniques, etc.) related to data science, all in one place. Homework 3

ELI5 is a Python package which helps to debug machine learning classifiers and explain their predictions. It provides support for the following machine learning frameworks and packages: License is MI…

This repository contains all the code, notebooks and examples used in this book. We will also be adding bonus content here from time to time. So keep watching this space! Master the essential skills …

In detail, the following community detection and embedding methods were implemented. Karate Club can be installed with the following pip command. As we create new releases frequently, upgrading the p…

Automated machine learning for production and analytics auto_ml is designed for production. Here's an example that includes serializing and loading the trained model, then getting predictions on sing…

Text analytics can be a bit overwhelming and frustrating at times with the unstructured and noisy nature of textual data and the vast amount of information available. "Text Analytics with Python" is …

While I would not live trade based off of the predictions from this exact code, I do believe that you can use this project as starting point for a profitable trading system – I have actually used cod…

If you use metric-learn in a scientific publication, we would appreciate citations to the following paper: Bibtex entry:

Python implementations of the k-modes and k-prototypes clustering algorithms. Relies on numpy for a lot of the heavy lifting. k-modes is used for clustering categorical variables. It defines clusters…

You will get the following, mlwp-test is the env name used in this example The environment is successfully created. Now activate the Conda environment. You will get the following Install the required…

With Intel(R) Extension for Scikit-learn, you can: To install Intel(R) Extension for Scikit-learn, run: The software acceleration is achieved through patching. It means, replacing the stock scikit-le…

You should already have pip, Python, and optionally XGBoost, LightGBM, and CatBoost installed on your system (see below). Run the following command to install AlphaPy: Pyfolio is automatically instal…

A few different ways are available:

To demo some of Otto's core features, try out the following: And feel free to get creative! Come up with your own machine learning goals and see where Otto takes you. Below is a step-by-step breakdow…

To run the examples, jupyter is required. Pre-release, experimental builds containing recently added features, and/or bug fixes can be installed by running After developer installation, you can launc…

The result is The equivalent using only Python is The expression using Awkward Arrays is more concise, using idioms familiar from NumPy, and it also has NumPy-like performance. For a similar problem …