2018-英特尔-人工智能学生套件 #Course#: 通过我们为软件开发人员、数据科学家和学生制作的免费课程学习人工智能理论并跟随动手练习。 这些课程涵盖人工智能论题，并探讨在个人计算机和服务器工作站中利用英特尔 ® 处理器的工具和优化的库。
stanford-cs-221-artificial-intelligence: VIP cheatsheets for Stanford's CS 221 Artificial Intelligence
Virgilio #Course#: Your new Mentor for Data Science E-Learning.
2010-MIT Artifical Intelligence Videos #Course#: This course includes interactive demonstrations which are intended to stimulate interest and to help students gain intuition about how artificial intelligence methods work under a variety of circumstances.
2014-斯坦福-机器学习课程 #Course#: 在本课程中，您将学习最高效的机器学习技术，了解如何使用这些技术，并自己动手实践这些技术。更重要的是，您将不仅将学习理论知识，还将学习如何实践，如何快速使用强大的技术来解决新问题。最后，您将了解在硅谷企业如何在机器学习和 AI 领域进行创新。 Here is Python assignments for the machine learning class by andrew ng on coursera with complete submission for grading capability and re-written instructions. 吴恩达的 CS229，有人把它浓缩成 6 张中文速查表！。
2014-Statistical Learning (Self-Paced) #Course#: This is an introductory-level course in supervised learning, with a focus on regression and classification methods.
2015-Udacity-Intro to Artificial Intelligence #Course#: In this course, you’ll learn the basics of modern AI as well as some of the representative applications of AI. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination.
2016-台大机器学习技法 #Course#: Linear Support Vector Machine (SVM) :: Course Introduction @ Machine Learning Techniques, etc.
2017-EdX-Artificial Intelligence (AI) #Course#: Learn the fundamentals of Artificial Intelligence (AI), and apply them. Design intelligent agents to solve real-world problems including, search, games, machine learning, logic, and constraint satisfaction problems.
2017-Advanced Machine Learning #Course#: Deep Dive Into The Modern AI Techniques. You will teach computer to see, draw, read, talk, play games and solve industry problems.
2018-Machine Learning Crash Course with TensorFlow APIs by Google #Course#: Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.
2018-Foundations of Machine Learning #Course#: Understand the Concepts, Techniques and Mathematical Frameworks Used by Experts in Machine Learning.
2018-100 Days Of ML Code #Series#: 100 Days of ML Coding
2018-Machine Learning and Medicine #Course#: This is a not-particularly-systematic attempt to curate a handful of my favorite resources for learning statistics and machine learning.
2018-mlcourse.ai #Course#: Open Machine Learning Course
2019-台大教授李宏毅的机器学习课程: 台大教授李宏毅的机器学习课程经常被认为是中文开放课程中的首选。李教授的授课风格风趣幽默，通俗易懂，其课程内容中不仅有机器学习、深度学习的基础知识，也会介绍 ML 领域里的各种最新技术。
2016-Deep Learning by Google #Course#: In this course, you’ll develop a clear understanding of the motivation for deep learning, and design intelligent systems that learn from complex and/or large-scale datasets.
2017-CS 20SI: TensorFlow for Deep Learning Research #Course#: This course will cover the fundamentals and contemporary usage of the TensorFlow library for deep learning research. We aim to help students understand the graphical computational model of TensorFlow, explore the functions it has to offer, and learn how to build and structure models best suited for a deep learning project.
2017-Fast.ai DeepLearning AI #Course#: Most of the library is quite well tested since many students have used it to complete the Practical Deep Learning for Coders course. However it hasn't been widely used yet outside of the course, so you may find some missing features or rough edges. Personal notes can be found here; 关联的课件、代码等资源可以查看这里。
2018-Deep Learning Specialization #Course#: Deep Learning is transforming multiple industries. This five-course specialization will help you understand Deep Learning fundamentals, apply them, and build a career in AI.
2018-Deep Reinforcement Learning Course: Deep Reinforcement Learning Course is a free series of blog posts and videos 🆕 about Deep Reinforcement Learning, where we'll learn the main algorithms, and how to implement them with Tensorflow.
2016-University of Illinois at Urbana-Champaign:Text Mining and Analytics #Course#: This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort.
2017-Neural Networks for Machine Learning #Course#: Learn about artificial neural networks and how they're being used for machine learning, as applied to speech and object recognition, image segmentation, modeling language and human motion, etc.
2017-Oxford Deep NLP course #Course#: This is an advanced course on natural language processing. Automatically processing natural language inputs and producing language outputs is a key component of Artificial General Intelligence.
2017-CS224d: Deep Learning for Natural Language Processing #Course#: Intro to NLP and Deep Learning, Simple Word Vector representations: word2vec, GloVe, etc.
2017-Artificial Intelligence for Robotics #Course#: Learn how to program all the major systems of a robotic car from the leader of Google and Stanford's autonomous driving teams.
A self driving toy car using end-to-end learning #Project#: To make a lane follower based on a standard RC car using Raspberry Pi and a camera. The software is a simple Convolutional Network, which takes in the image fetched from the camera and outputs the steering angle.
2015-Lectures on scientific computing with Python: Lectures on scientific computing with python, as IPython notebooks.
2017-All-in-one Docker image for Deep Learning #Project#: An all-in-one Docker image for deep learning. Contains all the popular DL frameworks (TensorFlow, Theano, Torch, Caffe, etc.)
2017-Top Deep Learning Projects: A list of popular github projects related to deep learning (ranked by stars).
2018-Seedbank #Project#: Browse seeds from a list of different kinds of machine learning examples and use the top level categories to narrow your search according to your interests.
numpy-ml #Project#: Ever wish you had an inefficient but somewhat legible collection of machine learning algorithms implemented exclusively in numpy? No?
AI Playbook: This site is designed as a resource for anyone asking those questions, complete with examples and sample code to help you get started.
2015-TensorFlow Examples: This tutorial was designed for easily diving into TensorFlow, through examples. For readability, it includes both notebooks and source codes with explanation.
2016-Deep Learning Using TensorFlow: This repository contains the code for TensorFlow Tutorials for Deep Learning from Starting to End. All the code is written using Python3.
2017-Deep Learning 21 Examples: 本工程是《21 个项目玩转深度学习———基于 TensorFlow 的实践详解》的配套代码，代码推荐的运行环境为：Ubuntu 14.04，Python 2.7、TensorFlow >= 1.4.0。请尽量使用类 UNIX 系统和 Python 2 运行本书的代码。
2017-TensorFlow Models by Sarasra #Project#: This repository contains a number of different models implemented in TensorFlow: the official models, the research models, the samples folder and the tutorials folder.
Android TensorFlow Machine Learning Example #Project#: This article is for those who are already familiar with machine learning and know how to the build model for machine learning(for this example I will be using a pre-trained model).
2017-NakedTensor #Project#: Bare bone examples of machine learning in TensorFlow.
2018-Deep Learning Using TensorFlow #Project#: This repository contains the code for TensorFlow Tutorials for Deep Learning from Starting to End. All the code is written using Python3.
2018-TensorFlow Project Template #Project#: A simple and well designed structure is essential for any Deep Learning project, so after a lot of practice and contributing in tensorflow projects here's a tensorflow project template that combines simplcity, best practice for folder structure and good OOP design.
2018-finch #Project#: Make NLP Flow in TensorFlow
2019-TensorFlow in Practice #Course#: In this four-course Specialization, you’ll explore exciting opportunities for AI applications. Begin by developing an understanding of how to build and train neural networks.
Pytorch Examples: A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.
PyTorch Tutorial #Project#: This repository provides tutorial code for deep learning researchers to learn PyTorch. In the tutorial, most of the models were implemented with less than 30 lines of code. Before starting this tutorial, it is recommended to finish Official Pytorch Tutorial.
2015-Trained image classification models for Keras #Project#: Keras code and weights files for popular deep learning models.
2014-Kaggle Tutorial: 基于旅馆推荐比赛实例的完整教程。