<commit_before>Machine Learning
----------------
* [A Course in Machine Learning](http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf) (PDF)
* [A First Encounter with Machine Learning](https://www.ics.uci.edu/~welling/teaching/ICS273Afall11/IntroMLBook.pdf) (PDF)
* [AI Algorithms, Data Structures, and Idioms in Prolog, Lisp, and Java](http://wps.aw.com/wps/media/objects/5771/5909832/PDF/Luger_0136070477_1.pdf) - <NAME>, <NAME>
* [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) - <NAME>, <NAME>, <NAME> and <NAME>
* [Artificial Intelligence | Machine Learning](http://see.stanford.edu/see/materials/aimlcs229/handouts.aspx) - Andrew Ng *(Notes, lectures, and problems)*
* [Machine Learning | Standford] (https://see.stanford.edu/Course/CS229) - Andrew Ng *(Notes, lectures and videos)*
* [Artificial Intelligence A Modern Approach](http://51lica.com/wp-content/uploads/2012/05/Artificial-Intelligence-A-Modern-Approach-3rd-Edition.pdf) (PDF)
* [Bayesian Reasoning and Machine Learning](http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage)
* [Computer Vision: Algorithms and Applications](http://hackershelf.com/book/134/computer-vision-algorithms-and-applications/)
* [Gaussian Processes for Machine Learning](http://www.gaussianprocess.org/gpml/)
* [Inductive Logic Programming](http://www-ai.ijs.si/SasoDzeroski/ILPBook/)
* [Information Theory, Inference, and Learning Algorithms](http://www.inference.phy.cam.ac.uk/itila/)
* [Introduction to Machine Learning](http://alex.smola.org/drafts/thebook.pdf) - <NAME> and <NAME> (PDF)
* [Introduction to Machine Learning](http://arxiv.org/abs/0904.3664v1) - <NAME>
* [Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf) (PDF)
* [Machine Learning](http://www.intechopen.com/books/machine_learning)
* [Machine Learning, Neural and Statistical Classification](http://www1.maths.leeds.ac.uk/~charles/statlog/whole.pdf) (PDF) or [online version](http://www1.maths.leeds.ac.uk/~charles/statlog/) - This book is based on the EC (ESPRIT) project StatLog.
* [Natural Language Processing with Python](http://victoria.lviv.ua/html/fl5/NaturalLanguageProcessingWithPython.pdf) -  <NAME>, <NAME>, and <NAME> (PDF)
* [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com)
* [Probabilistic Models in the Study of Language](http://idiom.ucsd.edu/~rlevy/pmsl_textbook/text.html) (Draft, with R code)
* [Programming Computer Vision with Python](http://programmingcomputervision.com/) - <NAME>
* [Reinforcement Learning: An Introduction](http://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.html)
* [The Elements of Statistical Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/) - <NAME>, <NAME>, and <NAME>
* [The LION Way: Machine Learning plus Intelligent Optimization](http://www.e-booksdirectory.com/details.php?ebook=9575)
* [The Python Game Book](http://thepythongamebook.com/en%3Astart)

Websites
-------
* [Kaggle: The Home of Data Science](https://www.kaggle.com/)
* [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/)
* [Welcome to Deep Learning](http://deeplearning.net/)
* [Excellent Answer by Franck-Dernoncourt](https://www.quora.com/What-are-the-best-talks-lectures-related-to-big-data-algorithms-machine-learning/answer/Franck-Dernoncourt)
* [UCI | Machine Learning Repository] (http://archive.ics.uci.edu/ml/)
* [Machine Learning Demos](http://mldemos.epfl.ch/)
* [AI WEBSITES THAT DESIGN THEMSELVES](https://thegrid.io/)
* [A Visual Introduction to Machine Learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/http://www.kdnuggets.com/)
* [Machine Learning in Games](http://satirist.org/learn-game/)
* [Data Mining, Analytics, Big Data, and Data Science](http://www.kdnuggets.com/)


Libraries
--------
Python:
* [Scikit-learn](http://scikit-learn.org/stable/):
* [PyBrain](http://pybrain.org/)
* [Natural Language Toolkit(nltk)](http://www.nltk.org/)
* [Theano](http://www.deeplearning.net/software/theano/)
* [Caffe(Deep learning framework by the BVLC)](http://caffe.berkeleyvision.org/)
* [Pylearn2](http://deeplearning.net/software/pylearn2/)
* [MDP (Modular toolkit for Data Processing)](http://mdp-toolkit.sourceforge.net/): 
* [TensorFlow™ ](https://www.tensorflow.org/)
* [Spark](http://spark.apache.org/)
* [Milk](https://pypi.python.org/pypi/milk/)
* [OpenCV(object detection stuff)](http://opencv.willowgarage.com/documentation/python/index.html)
* [Machine Learning Python](http://mlpy.sourceforge.net/)
* [LIBSVM -- A Library for Support Vector Machines](https://www.csie.ntu.edu.tw/~cjlin/libsvm/)

Algorithms
----------<commit_msg>Fixed a link<commit_after>Machine Learning
----------------
* [A Course in Machine Learning](http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf) (PDF)
* [A First Encounter with Machine Learning](https://www.ics.uci.edu/~welling/teaching/ICS273Afall11/IntroMLBook.pdf) (PDF)
* [AI Algorithms, Data Structures, and Idioms in Prolog, Lisp, and Java](http://wps.aw.com/wps/media/objects/5771/5909832/PDF/Luger_0136070477_1.pdf) - <NAME>, <NAME>
* [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) - <NAME>, <NAME>, <NAME> and <NAME>
* [Artificial Intelligence | Machine Learning](http://see.stanford.edu/see/materials/aimlcs229/handouts.aspx) - Andrew Ng *(Notes, lectures, and problems)*
* [Machine Learning | Standford] (https://see.stanford.edu/Course/CS229) - Andrew Ng *(Notes, lectures and videos)*
* [Artificial Intelligence A Modern Approach](http://51lica.com/wp-content/uploads/2012/05/Artificial-Intelligence-A-Modern-Approach-3rd-Edition.pdf) (PDF)
* [Bayesian Reasoning and Machine Learning](http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage)
* [Computer Vision: Algorithms and Applications](http://hackershelf.com/book/134/computer-vision-algorithms-and-applications/)
* [Gaussian Processes for Machine Learning](http://www.gaussianprocess.org/gpml/)
* [Inductive Logic Programming](http://www-ai.ijs.si/SasoDzeroski/ILPBook/)
* [Information Theory, Inference, and Learning Algorithms](http://www.inference.phy.cam.ac.uk/itila/)
* [Introduction to Machine Learning](http://alex.smola.org/drafts/thebook.pdf) - <NAME> and <NAME> (PDF)
* [Introduction to Machine Learning](http://arxiv.org/abs/0904.3664v1) - <NAME>
* [Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf) (PDF)
* [Machine Learning](http://www.intechopen.com/books/machine_learning)
* [Machine Learning, Neural and Statistical Classification](http://www1.maths.leeds.ac.uk/~charles/statlog/whole.pdf) (PDF) or [online version](http://www1.maths.leeds.ac.uk/~charles/statlog/) - This book is based on the EC (ESPRIT) project StatLog.
* [Natural Language Processing with Python](http://victoria.lviv.ua/html/fl5/NaturalLanguageProcessingWithPython.pdf) -  <NAME>, <NAME>, and <NAME> (PDF)
* [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com)
* [Probabilistic Models in the Study of Language](http://idiom.ucsd.edu/~rlevy/pmsl_textbook/text.html) (Draft, with R code)
* [Programming Computer Vision with Python](http://programmingcomputervision.com/) - <NAME>
* [Reinforcement Learning: An Introduction](http://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.html)
* [The Elements of Statistical Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/) - <NAME>, <NAME>, and <NAME>
* [The LION Way: Machine Learning plus Intelligent Optimization](http://www.e-booksdirectory.com/details.php?ebook=9575)
* [The Python Game Book](http://thepythongamebook.com/en%3Astart)

Websites
-------
* [Kaggle: The Home of Data Science](https://www.kaggle.com/)
* [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/)
* [Welcome to Deep Learning](http://deeplearning.net/)
* [Excellent Answer by Franck-Dernoncourt](https://www.quora.com/What-are-the-best-talks-lectures-related-to-big-data-algorithms-machine-learning/answer/Franck-Dernoncourt)
* [Machine Learning Demos](http://mldemos.epfl.ch/)
* [AI WEBSITES THAT DESIGN THEMSELVES](https://thegrid.io/)
* [A Visual Introduction to Machine Learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)
* [Machine Learning in Games](http://satirist.org/learn-game/)
* [Data Mining, Analytics, Big Data, and Data Science](http://www.kdnuggets.com/)


Libraries
--------
Python:
* [Scikit-learn](http://scikit-learn.org/stable/):
* [PyBrain](http://pybrain.org/)
* [Natural Language Toolkit(nltk)](http://www.nltk.org/)
* [Theano](http://www.deeplearning.net/software/theano/)
* [Caffe(Deep learning framework by the BVLC)](http://caffe.berkeleyvision.org/)
* [Pylearn2](http://deeplearning.net/software/pylearn2/)
* [MDP (Modular toolkit for Data Processing)](http://mdp-toolkit.sourceforge.net/): 
* [TensorFlow™ ](https://www.tensorflow.org/)
* [Spark](http://spark.apache.org/)
* [Milk](https://pypi.python.org/pypi/milk/)
* [OpenCV(object detection stuff)](http://opencv.willowgarage.com/documentation/python/index.html)
* [Machine Learning Python](http://mlpy.sourceforge.net/)
* [LIBSVM -- A Library for Support Vector Machines](https://www.csie.ntu.edu.tw/~cjlin/libsvm/)

Algorithms
----------
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{'dir': 'git-commits-cleaned', 'id': '6481951', 'n_tokens_mistral': 3375, 'n_tokens_neox': 3132, 'n_words': 547}