lddyato Goto Github PK

06_statistics-inference

08-practical-machine-learning

09-shiny-project

algorithm

The beauty of Algorithm

analytics_vidhya

Codes related to activities on AV including articles, hackathons and discussions.

analyticsvidhya_3.x_hackathon

Winning Solution for Analytics Vidhya Hackathon 3.X

applied-data-science-in-python

applied-machine-learning-in-python

Solutions to the 'Applied Machine Learning In Python' Coursera course exercises

appliedml_python_coursera

Material and note of the course of Applied ML in Python

capstoneproject

For Coursera Data Science Capstone Project

cookbook-code

Recipes of the IPython Cookbook, the definitive guide to high-performance scientific computing and data science in Python

coursera-machine-learning

Coursera Machine Learning - Python code

coursera-ml-andrewng

use numpy, scipy, and tensorflow to implement these basic ML model and learning algorithm

coursera-r-programming

Johns Hopkins University R Programming by Roger D. Peng, PhD August 4 - September 1 2014

coursera-using-python-access-data

data-scientist-s-toolbox

datasharing

The Leek group guide to data sharing

deeplearning

Deep Learning introduction and its application in various fields

ds-cheatsheets

List of Data Science Cheatsheets to rule the world

exdata_plotting1

Plotting Assignment 1 for Exploratory Data Analysis

exploratory-data-analysis

getting-and-cleaning-data

ghtoc

generate toc for markdown file

how-to-use-git-and-github

introduction-to-data-science-in-python

This repository contains Ipython notebooks of assignments and tutorials used in the course introduction to data science in python, part of Applied Data Science using Python Specialization from University of Michigan offered by Coursera

jieba

jieba + nltk

jupyter-themes

Custom Jupyter Notebook Themes

kmodes

Python implementations of the k-modes and k-prototypes clustering algorithms, for clustering categorical data

leetcode

Solutions to LeetCode by Java

machine-learning-algorithms

This repo contains some main machine learning algorithms by using mind mapping to better understand their concepts, basic ideas and models, i.e. Regularized Regression, Logistic Regression, Decision Tree, Naive Bayes, SVM, KNN, K-means Clustering, Ensemble Learning (Adaboost, Boosting Tree, Random Forest, GDBT).