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boilerplate

🍪 Cookiecutter template for production level API services with machine learning.

data-science-ipython-notebooks

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

dkhpb

dramatiq-abort

Dramatiq extension to abort message

hermione

ML made simple

ibc

Automation of Interactive Brokers TWS. You can download the latest release here: https://github.com/ibcalpha/ibc/releases/latest

kaggle-binder

Use the Kaggle kernels environment on mybinder.org

letsexplorepython

import from https://gitlab.com/mayankjohri/LetsExplorePython

prefect

The Prefect Core automation engine

presentations

Repo for simple RISE presentations

python-colab01

python-conda_pip

Installing packages with pip and conda

python-conda_pip02

starter-framework-for-machine-learning-projects

In this article I'll demonstrate some sort of a framework for working on machine learning projects. As you may know, machine learning in general is about extracting knowledge from data therefore, most of machine learning projects will depend on a data collection - called dataset - from a specific domain on which, we are investigating a certain problem to build a predictive model suitable for it. This model should follow certain set of steps to accomplish its purpose, In the following sections I will practically, introduce a simplified clarification about the main steps for performing statistical learning or building machine learning model

tst

public test