machine-learning/courses.md

The following is a list of free or paid online courses on machine learning, statistics, data-mining, etc.

## Machine-Learning / Data Mining

* [Artificial Intelligence (Columbia University)](https://www.edx.org/course/artificial-intelligence-ai-columbiax-csmm-101x-0) - free
* [Machine Learning (Columbia University)](https://www.edx.org/course/machine-learning-columbiax-csmm-102x-0) - free
* [Machine Learning (Stanford University)](https://www.coursera.org/learn/machine-learning) - free
* [Neural Networks for Machine Learning (University of Toronto)](https://www.coursera.org/learn/neural-networks) - free. Also [available on YouTube](https://www.youtube.com/watch?v=cbeTc-Urqak&list=PLYvFQm7QY5Fy28dST8-qqzJjXr83NKWAr) as a playlist. #This course is no longer available on Coursera.
* [Deep Learning Specialization (by Andrew Ng, deeplearning.ai)](https://www.coursera.org/specializations/deep-learning) - Courses: I Neural Networks and Deep Learning; II Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; III Structuring Machine Learning Projects; IV Convolutional Neural Networks; V Sequence Models; Paid for grading/certification, financial aid available, free to audit
* [Deep Learning Nano Degree on Udacity](https://www.udacity.com/course/deep-learning-nanodegree--nd101) - $
* [Intro to Deep Learning (MIT)](http://introtodeeplearning.com/)
* [Stanford's CS20 Tensorflow for Deep Learning Research](http://web.stanford.edu/class/cs20si/)
* [fast.ai](https://www.fast.ai/) - deep learning MOOC 
* [Machine Learning Specialization (University of Washington)](https://www.coursera.org/specializations/machine-learning) - Courses: Machine Learning Foundations: A Case Study Approach, Machine Learning: Regression, Machine Learning: Classification, Machine Learning: Clustering & Retrieval, Machine Learning: Recommender Systems & Dimensionality Reduction,Machine Learning Capstone: An Intelligent Application with Deep Learning; free
* [Machine Learning Course (2014-15 session) (by Nando de Freitas, University of Oxford)](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/) - Lecture slides and video recordings.
* [Learning from Data (by Yaser S. Abu-Mostafa, Caltech)](http://www.work.caltech.edu/telecourse.html) - Lecture videos available
* [Intro to Machine Learning](https://www.udacity.com/course/intro-to-machine-learning--ud120) - free
* [Probabilistic Graphical Models (by Prof. Daphne Koller, Stanford)](https://www.coursera.org/specializations/probabilistic-graphical-models) Coursera Specialization
* [Reinforcement Learning Course (by David Silver, DeepMind)](https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLzuuYNsE1EZAXYR4FJ75jcJseBmo4KQ9-) - YouTube playlist and [lecture slides](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html).
* [Keras in Motion](https://www.manning.com/livevideo/keras-in-motion) $
* [Stanford's CS231n: CNNs for Visual Recognition](https://www.youtube.com/watch?v=vT1JzLTH4G4&index=1&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv) - Spring 2017 iteration, instructors (Fei-Fei Li, Justin Johnson, Serena Yeung), or [Winter 2016 edition](https://www.youtube.com/watch?v=NfnWJUyUJYU&list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC) instructors (Fei-Fei Li, Andrej Karpathy, Justin Johnson). [Course website](http://cs231n.github.io/) has supporting material.
* [University of California, Berkeley's CS294: Deep Reinforcement Learning](https://www.youtube.com/watch?v=8jQIKgTzQd4&list=PLkFD6_40KJIwTmSbCv9OVJB3YaO4sFwkX) - Fall 2017 edition. [Course website](http://rll.berkeley.edu/deeprlcourse/) has lecture slides and other related material.
* [Machine Learning (Georgia Tech) on Udacity](https://www.udacity.com/course/machine-learning--ud262) - free
* [Reinforcement Learning (Georgia Tech) on Udacity ](https://www.udacity.com/course/reinforcement-learning--ud600) - free
* [Machine Learning for Trading](https://www.udacity.com/course/machine-learning-for-trading--ud501) - free
* [Mining of Massive Datasets](https://www.youtube.com/watch?v=xoA5v9AO7S0&list=PLLssT5z_DsK9JDLcT8T62VtzwyW9LNepV) (YouTube playlist) - Course [website](http://mmds.org/) has info about accompanying book, free chapters, and Stanford's [MOOC](https://lagunita.stanford.edu/courses/course-v1:ComputerScience+MMDS+SelfPaced/about)
* [Machine Learning Crash Course (Google)](https://developers.google.com/machine-learning/crash-course/) - free
* [Machine Learning Mini Bootcamp Course (LambdaSchool)](https://lambdaschool.com/courses/data-science/intro/) - free and $
* [Microsoft Professional Program for Artificial Intelligence](https://academy.microsoft.com/en-us/professional-program/tracks/artificial-intelligence/) - free 
* [Open Machine Learning Course](https://github.com/Yorko/mlcourse.ai) with [articles](https://medium.com/open-machine-learning-course) on Medium 
* [Machine Learning A-Z (Udemy)](https://www.udemy.com/machinelearning/) - Hands-On Python & R In Data Science
* [Deep Learning Crash Course](https://www.manning.com/livevideo/deep-learning-crash-course) - $
* [Reinforcement Learning in Motion](https://www.manning.com/livevideo/reinforcement-learning-in-motion) - $
* [Udemy A-Z Machine learning course](https://www.udemy.com/course/machinelearning/) - $
* [Statistics and Probability-Khan Academy](https://www.khanacademy.org/math/statistics-probability) - $