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=== Learn about Data Science and Machine Learning === ===== Classes ===== *[https://www.coursera.org/course/ml Coursera Machine Learning Course with Andrew Ng] *[https://www.coursera.org/course/compneuro Coursera Computational Neuroscience Course with Rajesh P N Rao and Adrienne Fairhall] *[http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/ MIT Machine Learning Class with Tommi Jaakkola] *[http://cs229.stanford.edu/materials.html Stanford CS229] *[http://www.cs.cmu.edu/~tom/10701_sp11/lectures.shtml Carnegie Mellon Machine Learning Course with Tom Mitchell] *[http://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/ Linear Algebra with Gilbert Strang] *[https://www.youtube.com/playlist?list=PL6Xpj9I5qXYEcOhn7TqghAJ6NAPrNmUBH Neural Networks Class with Hugo Larochelle] *[https://introtodeeplearning.com/ MIT Introduction to Deep Learning] * [https://course.fast.ai/ Practical Deep Learning for Coders - Fast.ai ] ==== Books ==== *[http://statweb.stanford.edu/~tibs/ElemStatLearn/ Elements of Statistical Learning] *[https://www.google.com/search?client=ubuntu&channel=fs&q=pattern+recognition+and+machine+learning&ie=utf-8&oe=utf-8#channel=fs&q=pattern+recognition+and+machine+learning+pdf Pattern Recognition and Machine Learning] *[https://www.google.com/search?&channel=fs&q=+Information+Theory%2C+Inference%2C+and+Learning+Algorithms.&ie=utf-8&oe=utf-8#channel=fs&q=Information+Theory%2C+Inference%2C+and+Learning+Algorithms+pdf Information Theory, Inference, and Learning Algorithms] *[http://chimera.labs.oreilly.com/books/1230000000345 Interactive Data Visualization for the Web (D3)] *[http://cran.r-project.org/doc/manuals/R-intro.pdf Introduction to R] *[http://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/amsbook.mac.pdf Introduction to Probability (Grinstead and Snell)] *[http://www.cis.temple.edu/~latecki/Courses/CIS2033-Spring12/A_modern_intro_probability_statistics_Dekking05.pdf Modern Introduction to Probability and Statistics (Kraaikamp and Meester)] *[http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/090310.pdf Bayesian Reasoning and Machine Learning] *[https://github.com/chandanverma07/Ebooks/blob/master/Deep%20Learning%20with%20Python%2C%20Fran%C3%A7ois%20Chollet.pdf Deep Learning with Python François Chollet] ==== Tutorials ==== *[http://nbviewer.ipython.org/github/unpingco/Python-for-Signal-Processing/tree/master/ Signal Processing IPython Notebooks] *[http://scikit-learn.org/stable/tutorial/basic/tutorial.html Introduction to ML with scikits.learn] *[http://www.sagemath.org/doc/tutorial/ Learn how to use SAGE] *[https://skillcombo.com/topic/machine-learning/ Online Machine Learning Courses] ==== Noisebridge ML Class Slides ==== *[[NBML/Workshops/Intro to Machine Learning|Intro to Machine Learning]] *[[NBML/Workshops/Brief Tour of Statistics|A Brief Tour of Statistics]] *[[NBML/Workshops/Generalized Linear Models|Generalized Linear Models]] *[[NBML/Workshops/Neural Nets|Neural Nets Workshop]] *[[NBML/Workshops/Support Vector Machines|Support Vector Machines]] *[[NBML/Workshops/Random Forests|Random Forests]] *[[NBML/Workshops/Independent Components Analysis|Independent Components Analysis]] *[[NBML/Workshops/Deep Nets|Deep Nets]]
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