mirror of
https://github.com/josephmisiti/awesome-machine-learning.git
synced 2024-11-23 21:23:37 +03:00
22 lines
3.6 KiB
Markdown
22 lines
3.6 KiB
Markdown
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) - free
|
|
* [Machine Learning (Columbia University)](https://www.edx.org/course/machine-learning-columbiax-csmm-102x) - 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.
|
|
* [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
|
|
* [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 or [this Youtube playlist](https://www.youtube.com/watch?v=WPSQfOkb1M8&list=PL50E6E80E8525B59C) if you can't afford the enrollment fee.
|
|
* [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](https://www.udacity.com/course/machine-learning--ud262) - free
|
|
* [Reinforcement Learning](https://www.udacity.com/course/reinforcement-learning--ud600) - free
|
|
* [Machine Learning for Trading](https://www.udacity.com/course/machine-learning-for-trading--ud501) - free
|