Credit Risk analysis by using Python and ML

Practice and tutorial-style notebooks covering wide variety of machine learning techniques

Example PyMC3 project for performing Bayesian data analysis using a probabilistic programming approach to machine learning.

Курс "Введение в машинное обучение" (ВМК, МГУ имени М.В. Ломоносова)

Machine Learning Containers for NVIDIA Jetson and JetPack-L4T

Parallel computing with task scheduling

Python Cheat Sheet NumPy, Matplotlib

Alpine Container for Machine Learning

A Case Study Approach to Successful Data Science Projects Using Python, Pandas, and Scikit-Learn

Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.

For extensive instructor led learning

datascienv is package that helps you to setup your environment in single line of code with all dependency and it is also include pyforest that provide single line of import all required ml libraries

A collection of notebooks of my Machine Learning class written in python 3

Small Docker image with Python Machine Learning tools (~180MB) https://hub.docker.com/r/frolvlad/alpine-python-machinelearning/

Open Machine Learning Course

Study E-Book(ComputerVision DeepLearning MachineLearning Math NLP Python ReinforcementLearning)

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.

The book was written and tested with Python 3.5, though other Python versions (including Python 2.7) should work in nearly all cases.

📚 Various cheatsheets in PDF

🍊 📊 💡 Orange: Interactive data analysis