A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.

machine-learning

Content for Udacity's Machine Learning curriculum

machine-learning-project

Implemented a complete machine learning pipeline to solve a supervised regression problem

microservices-with-node-js-and-react

Microservices with Node JS and React

mlnd_boston_housing

Boston Housing - Udacity Machine Learning Nanodegree Project

modern-cpp-features

A cheatsheet of modern C++ language and library features.

moderncpp

Code for my Modern C++ webinar

mutual-fund-analysis

Analysis of Mutual Fund performance in India

nteract

📘 Desktop, Jupyter Extension, libraries, and more

public-health

⚕ Tutorials for public health crossfilter dashboards

pydata-berlin-2018-pygam

slides for pyGAM at PyData Berlin 2018

pykiteconnect

The official Python client library for the Kite Connect trading APIs

python-for-data-science-and-machine-learning-bootcamp---udemy

Course materials for the Python for Data Science and Machine Learning Bootcamp MOOC on Udemy

redash

Make Your Company Data Driven. Connect to any data source, easily visualize and share your data.

scikit-learn

scikit-learn: machine learning in Python

stockpredictionai

In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.

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