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100-Days-Of-ML-Code中文版
Course materials and handouts for #100DaysOfCode in Python course
test automation projects
A collection of important graph embedding, classification and representation learning papers with implementations.
A curated list of awesome Machine Learning frameworks, libraries and software.
Code from Full Stack Python books & videos, such as Deploying Flask Web Applications and Introduction to Ansible.
.pdf Format Books for Machine and Deep Learning
Course Files for Complete Python 3 Bootcamp Course on Udemy
Lectures for Udemy - Complete Python Bootcamp Course
The Data Engineering Cookbook
Notes and homework for Coursera's "Machine learning" online class :books:
Introduction to Git for Data Science by Greg Wilson
Introduction to machine learning and data mining How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention. Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike. This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques. Background We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed. (Most or all code should be Octave compatible, so you may use Octave if you prefer.) Textbook and Reading There is no required textbook for the class. However, useful books on the subject for supplementary reading include Murphy's "Machine Learning: A Probabilistic Perspective", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".
Repository of teaching materials, code, and data for my data analysis and machine learning projects.
The overall objective of this toolkit is to provide and offer a free collection of data analysis and machine learning that is specifically suited for doing data science. Its purpose is to get you started in a matter of minutes. You can run this collections either in Jupyter notebook or python alone.
Course demo code and other hand-out materials for our data driven web apps in Flask course
Cheat Sheets
Curriculum materials for General Assembly's "Data Science 101"
Examples and hacks inspired by the book Data Science from Scratch by Joel Grus
code for Data Science From Scratch book
A curated set of resources for data science, machine learning, artificial intelligence (AI), data and text analytics, data visualization, big data, and more.
A short tutorial for data scientists on how to write tests for code + data.
Curated list of Python resources for data science.
Machine learning datasets used in tutorials on MachineLearningMastery.com
Books for machine learning, deep learning, math, NLP, CV, RL, etc. 一些机器学习、深度学习等相关话题的书籍。
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.