ML

https://medium.com/ai³-theory-practice-business

https://dashbouquet.com/blog/web-development/progressive-tools-10-great-frameworks-and-libraries-for-ai

https://medium.mybridge.co/30-amazing-machine-learning-projects-for-the-past-year-v-2018-b853b8621ac7

https://vc.ru/31616-itogi-2017-goda-v-iskusstvennom-intellekte

https://habrahabr.ru/post/343800/

Вероятностная интерпретация классических моделей машинного обучения

https://habrahabr.ru/post/347184/

https://news.ycombinator.com/item?id=16493489

https://habrahabr.ru/post/332000/ Keras

https://spandan-madan.github.io/DeepLearningProject/

https://sadanand-singh.github.io/posts/treebasedmodels/

https://hackernoon.com/machine-learning-with-javascript-part-1-9b97f3ed4fe5

http://www.infoq.com/resource/minibooks/emag-machine-learning/en/pdf/The-InfoQ-eMag-Introduction-to-Machine-Learning.pdf

https://medium.com/@kailashahirwar/essential-cheat-sheets-for-machine-learning-and-deep-learning-researchers-efb6a8ebd2e5

http://blog.endpoint.com/2017/05/recognizing-handwritten-digits-quick.html

https://hackernoon.com/unsupervised-machine-learning-for-fun-profit-with-basket-clusters-17a1161e7aa1?12

https://habrahabr.ru/company/ods/blog/325654/ PCA , clustering

https://tryolabs.com/blog/2017/05/18/magazine-a-collection-of-our-machine-learning-articles/

https://www.reddit.com/r/MachineLearning+learnmachinelearning/

https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A

http://www.datasciencecentral.com/profiles/blogs/neural-networks-for-machine-learning

https://sites.google.com/site/mikelubinsky/ml101

https://sites.google.com/site/mikelubinsky/ml/machinelearning

https://sites.google.com/site/mikelubinsky/ml-1

https://habrahabr.ru/post/326656/

https://habrahabr.ru/company/spbifmo/blog/326894/

http://setosa.io/ev explain visually

https://code.facebook.com/posts/1373769912645926/faiss-a-library-for-efficient-similarity-search/

http://mlwhiz.com/blog/2017/03/26/top_data_science_resources_on_the_internet_right_now/

http://www.learndatasci.com/data-science-statistics-using-python/

https://tryolabs.com/blog/2016/12/06/major-advancements-deep-learning-2016/

https://arxiv.org/abs/1704.01568

probabilistic graphical models

https://news.ycombinator.com/item?id=13982860

https://news.ycombinator.com/item?id=14178399

https://medium.com/@ageitgey/try-deep-learning-in-python-now-with-a-fully-pre-configured-vm-1d97d4c3e9b

https://www.reddit.com/r/MachineLearning+learnmachinelearning/

https://elitedatascience.com/learn-machine-learning

https://habrahabr.ru/post/322188/ Обзор материалов по машинному обучению (13-20 февраля 2017)

https://habrahabr.ru/company/ods/blog/322076/ пример линейной регрессии

https://habrahabr.ru/company/ods/blog/323272/ Theano

https://habrahabr.ru/company/ods/blog/325422/

https://habrahabr.ru/company/ods/blog/325416/

https://habrahabr.ru/company/ods/blog/326418/

https://habrahabr.ru/company/google/blog/325896/

https://habrahabr.ru/company/wunderfund/

https://habrahabr.ru/post/324590/

http://mlexplore.org/

Основательный обзор классики машинного обучения и, конечно же, линейных моделей сделан в книге "Deep Learning" (I. Goodfellow, Y. Bengio, A. Courville, 2016);

Реализация многих алгоритмов машинного обучения с нуля – репозиторий rushter. Рекомендуем изучить реализацию логистической регрессии;

Курс Евгения Соколова по машинному обучению (материалы на GitHub). Хорошая теория, нужна неплохая математическая подготовка;

Курс Дмитрия Ефимова на GitHub (англ.). Тоже очень качественные материалы.

https://xyclade.github.io/MachineLearning/

https://blog.dominodatalab.com/fitting-gaussian-process-models-python/

https://cambridgespark.com/content

Online classes

https://www.datacamp.com/courses/unsupervised-learning-in-python

https://www.datacamp.com/community/blog/new-course-supervised-learning-with-scikit-learn

http://www.learndatasci.com/best-data-science-online-courses/

http://education.parrotprediction.teachable.com/

http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/

Apple

https://backchannel.com/an-exclusive-look-at-how-ai-and-machine-learning-work-at-apple-8dbfb131932b#.7q5fd02pr

SVM

https://sadanand-singh.github.io/posts/svmpython/

https://generalabstractnonsense.com/2017/03/A-quick-look-at-Support-Vector-Machines/

https://news.ycombinator.com/item?id=13939003

https://github.com/dennybritz/reinforcement-learning

http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-8/

https://www.reddit.com/r/MachineLearning/comments/5uks6d/d_github_repository_with_simple_implementation_of/

https://github.com/eriklindernoren/ML-From-Scratch

https://github.com/rushter/MLAlgorithms

https://drive.google.com/file/d/0B0RLknmL54khQlhGUzFUWEtncTA/view formulas

https://habrahabr.ru/company/wunderfund/blog/320482/

http://www.holehouse.org/mlclass/

https://habrahabr.ru/company/mailru/blog/321360/

http://iamtrask.github.io/

https://indico.io/blog/

higher bias and lower variance

“Ridge regression” will use all predictors in nal model whereas “Lasso regression” can be used for feature selection because coe cient values can be zero.

https://discuss.analyticsvidhya.com/t/di erence-between-ridge-regression-and-lasso-and-its-eect/3000

https://thomaswdinsmore.com/ best analytics blog

https://thomaswdinsmore.com/2017/01/16/the-year-in-machine-learning-part-four/

http://data36.com/ predictive analytics

http://www.unofficialgoogledatascience.com/

http://www.exploredata.net/Downloads/MINE-Application

KERAS

https://news.ycombinator.com/item?id=13872670

https://habrahabr.ru/company/ods/blog/325432/

https://github.com/dasguptar/treelstm.pytorch LSTM in pyTorch

Tensorflow

https://github.com/astorfi/TensorFlow-World-Resources

https://developers.google.com/machine-learning/crash-course/

http://cjalmeida.net/post/tensorflow-mnist/

https://www.youtube.com/watch?v=Rgpfk6eYxJA

https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10

https://habrahabr.ru/company/ods/blog/324898/

https://github.com/astorfi/TensorFlow-World-Resources

https://habrahabr.ru/post/326650/

https://machinelearningmastery.com/deep-learning-with-python/

http://adventuresinmachinelearning.com/python-tensorflow-tutorial/

https://escapethematrix.pl/mnist-the-totally-non-clickbait-beginnings/

https://github.com/silicon-valley-data-science/RNN-Tutorial

https://www.tensorflow.org/tutorials/

https://news.ycombinator.com/item?id=13859041

https://habrahabr.ru/post/321946/

https://cloud.google.com/blog/big-data/2017/01/learn-tensorflow-and-deep-learning-without-a-phd

http://www.svds.com/getting-started-deep-learning/

https://news.ycombinator.com/item?id=13653819

https://news.ycombinator.com/item?id=13655437

https://www.youtube.com/watch?v=hhJIztWR_vo

Natural Language Processing

https://www.coursera.org/learn/python-text-mining/lecture/zV9nP/naive-bayes-classifiers

https://github.com/oxford-cs-deepnlp-2017/lectures oxford deep NLP

https://habrahabr.ru/company/wunderfund/blog/318454/

https://medium.com/ai-society/jkljlj-7d6e699895c4#.edk6hbs2u

https://en.wikipedia.org/wiki/Language_model

https://arxiv.org/abs/1703.01619

https://medium.com/@ageitgey/machine-learning-is-fun-part-6-how-to-do-speech-recognition-with-deep-learning-28293c162f7a#.9fqae5wya

https://gab41.lab41.org/speech-recognition-you-down-with-ctc-8d3b558943f0#.hr6b4uakm

https://news.ycombinator.com/item?id=13588070

https://msdn.microsoft.com/en-us/library/mt762916.aspx

https://en.wikipedia.org/wiki/N-gram

https://tech.yandex.com/speechkit/cloud/doc/dg/concepts/speechkit-dg-recogn-docpage/

https://spacy.io/

https://habrahabr.ru/company/microsoft/blog/321494/

https://habrahabr.ru/company/wunderfund/blog/318454/

https://medium.com/@ageitgey/machine-learning-is-fun-part-5-language-translation-with-deep-learning-and-the-magic-of-sequences-2ace0acca0aa#.j8lvo1b5m

https://medium.com/intuitionmachine/the-two-paths-from-natural-language-processing-to-artificial-intelligence-d5384ddbfc18#.i8ny391c7

https://www.linkedin.com/in/spencerlin1

Word2vec

https://blog.acolyer.org/2016/04/21/the-amazing-power-of-word-vectors/

https://medium.com/@mishra.thedeepak/word2vec-in-minutes-gensim-nlp-python-6940f4e00980#.ep9xcrxa4

https://habrahabr.ru/post/324686/

http://www.deeplearningweekly.com/blog/demystifying-word2vec

https://medium.com/@mishra.thedeepak/doc2vec-in-a-simple-way-fa80bfe81104#.wqcqqp7qt

https://gab41.lab41.org/python2vec-word-embeddings-for-source-code-3d14d030fe8f#.4vbbixqlu

https://medium.com/ai-society/jkljlj-7d6e699895c4#.mfe6txjrm

Probabilistic programming

http://probabilistic-programming.org/wiki/Home

http://forestdb.org/ Generative models

https://news.ycombinator.com/item?id=13551271

http://blog.fastforwardlabs.com/2017/01/30/the-algorithms-behind-probabilistic-programming.html

https://arxiv.org/abs/1701.02434 Hamiltonian Monte Carlo

Deep learning

https://news.ycombinator.com/item?id=13540214

https://news.ycombinator.com/item?id=13773127

http://wiki.fast.ai/index.php/Course_notes

https://www.youtube.com/watch?v=N4gDikiec8E&feature=youtu.be

https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-choice

http://blog.yhat.com/posts/deep-learning-chess.html

https://github.com/AKSHAYUBHAT/DeepVideoAnalytics

How to approach any problem in machine learning

https://en.m.wikipedia.org/wiki/Cross_Industry_Standard_Process_for_Data_Mining

https://news.ycombinator.com/item?id=13821217

https://www.linkedin.com/pulse/approaching-almost-any-machine-learning-problem-abhishek-thakur#

https://habrahabr.ru/company/wunderfund/blog/320482/

http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf

https://www.springboard.com/blog/data-science-process/

https://www.springboard.com/resources/data-scientist-interview-guide

https://hackernoon.com/scikit-learn-cheat-sheet-python-machine-learning-ba69df05804d#.1j74jjfgj Many ML cheat sheets

http://aireads.top/

https://habrahabr.ru/post/320500/ KNIME

https://docs.google.com/spreadsheets/d/1AQvZ7-Kg0lSZtG1wlgbIsrm90HaTZrJGQMz-uKRRlFw/edit#gid=0 datasets for machine learning

https://www.analyticsvidhya.com/blog/2017/01/the-most-comprehensive-data-science-learning-plan-for-2017/

https://habrahabr.ru/post/319288/

https://habrahabr.ru/post/320726/ Random forest

https://alexanderdyakonov.wordpress.com/2016/11/14/

http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf

https://medium.com/louis-dorard/from-data-to-ai-with-the-machine-learning-canvas-part-i-d171b867b047#.wip3bn5i4

https://dzone.com/refcardz/machine-learning-predictive

https://docs.microsoft.com/en-in/azure/machine-learning/machine-learning-algorithm-cheat-sheet

https://github.com/rcompton/ml_cheat_sheet

http://www.jacksimpson.co/category/data-science/machine-learning/

http://www.jeannicholashould.com/what-i-learned-implementing-a-classifier-from-scratch.html

http://zderadicka.eu/revival-of-neural-networks/#more-1429

http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html

https://www.youtube.com/watch?v=2pWv7GOvuf0 Reinforcement learning

1. Deep Learning @ Udacity

http://course.fast.ai/start.html another deep learning

2. CS231n - Convolutional Neural Networks for Visual Recognition

3. CS224d - Deep Learning for Natural Language Processing

4. CS321 from Standford (outstanding material by Andrej Karpathy): http://cs231n.github.io/

- Hugo Larochelle videos: https://www.youtube.com/channel/UCiDouKcxRmAdc5OeZdiRwAg

- Michael Nielsen tutorial: http://neuralnetworksanddeeplearning.com/

- Chris Olah blog: http://colah.github.io/

- Keras blog: https://blog.keras.io/

http://work.caltech.edu/telecourse.html

- Goodfellow, Bengio, Courville: http://www.deeplearningbook.org/

https://www.tensorflow.org/versions/r0.10/tutorials/mnist/pros/

https://news.ycombinator.com/item?id=14543801 MNIST

https://github.com/humphd/have-fun-with-machine-learning

http://blog.datumbox.com/datumbox-machine-learning-framework-version-0-8-0-released/

https://www.pyimagesearch.com/ OpenCV

https://arxiv.org/abs/1612.07828 Apple's 1st machine learning paper

https://github.com/ZuzooVn/machine-learning-for-software-engineers/blob/master/README.md

http://languagengine.co/blog/symbolic-machine-learning/

https://iamtrask.github.io/2016/08/17/grokking-deep-learning/

https://habrahabr.ru/company/parallels/blog/317994/

https://blog.dominodatalab.com/video-huge-debate-r-vs-python-data-science/

https://github.com/josephmisiti/awesome-machine-learning/blob/master/README.md

https://habrahabr.ru/company/yandex/blog/316232/

https://news.ycombinator.com/item?id=12924020

https://github.com/ZuzooVn/machine-learning-for-software-engineers

https://news.ycombinator.com/item?id=12898718

http://eli5.readthedocs.io/en/latest/ Python library which allows to visualize and debug various Machine Learning models

https://blog.acolyer.org/2016/11/21/artificial-intelligence-and-life-in-2030/

Yan Cibulkin

http://aiukraine.com/speaker/yan-tsy-bul-kin/

https://www.youtube.com/watch?v=HOgk-86bQss

Sergei Nikolenko

https://www.youtube.com/watch?v=akpiJiu3Quo

https://www.youtube.com/watch?v=GfvTzLbDsNg

http://logic.pdmi.ras.ru/~sergey/slides/N16_AIUkraineDLNLP.pdf

http://logic.pdmi.ras.ru/~sergey/teaching/mlkfu2014.html Nikolenko lectures

http://www.unofficialgoogledatascience.com/2016/10/practical-advice-for-analysis-of-large.html

http://www.dataschool.io/15-hours-of-expert-machine-learning-videos/

http://arogozhnikov.github.io/2016/04/28/demonstrations-for-ml-courses.html

https://github.com/nipunbatra/ProgramaticallyUnderstandingSeries

https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/

http://www.datasciencecentral.com/

https://github.com/yandex/rep

http://www.datatau.com/ like hacker news

https://www.springboard.com/blog/data-mining-python-tutorial/

https://github.com/blue-yonder/tsfresh

https://github.com/rushter/MLAlgorithms

https://habrahabr.ru/company/wargaming/blog/270791/

https://www.youtube.com/channel/UCoFPJQg1Jesr021jLMody2w

https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/

Regularization

https://www.quora.com/What-is-the-difference-between-L1-and-L2-regularization

http://aoliver.org/why-mse

https://en.wikipedia.org/wiki/Predictive_analytics

https://en.wikipedia.org/wiki/Predictive_modelling

https://habrahabr.ru/post/313566/

t-SNE

https://news.ycombinator.com/item?id=12713388

https://habrahabr.ru/company/wunderfund/blog/326750/

https://en.wikipedia.org/wiki/Overfitting

https://www.youtube.com/watch?v=vNNcFTd_630&index=38&list=PL0Smm0jPm9WcCsYvbhPCdizqNKps69W4Z

https://www.reddit.com/r/learnmachinelearning/comments/5kbqh5/how_do_autoencoders_reduce_features/ Feature reduction

http://www.machinelearning.ru/wiki/

http://www.machinelearning.ru/wiki/index.php?title=%D0%9C%D0%B0%D1%88%D0%B8%D0%BD%D0%BD%D0%BE%D0%B5_%D0%BE%D0%B1%D1%83%D1%87%D0%B5%D0%BD%D0%B8%D0%B5_%28%D0%BA%D1%83%D1%80%D1%81_%D0%BB%D0%B5%D0%BA%D1%86%D0%B8%D0%B9%2C_%D0%9A.%D0%92.%D0%92%D0%BE%D1%80%D0%BE%D0%BD%D1%86%D0%BE%D0%B2%29

BOOKS

https://www.cs.cornell.edu/jeh/book2016June9.pdf

http://statweb.stanford.edu/~tibs/ElemStatLearn/

http://appliedpredictivemodeling.com/toc/ Applied predictive modelling

http://www-bcf.usc.edu/~gareth/ISL/ Intoduction To Statistical Lerning

http://www.mlyearning.org/ Ng

https://www.stat.cmu.edu/~cshalizi/ADAfaEPoV/ Cosma Shalizi book

http://www.qrg.northwestern.edu/BPS/readme.html Building Problem Solvers

http://mbmlbook.com/toc.html

https://manning.com/books/exploring-data-science

https://www.amazon.com/Making-Sense-Data-III-Visualizations/dp/0470536497

https://www.amazon.com/Introduction-Machine-Learning-Python-Scientists-ebook/dp/B01M0LNE8C/ref=dp_kinw_strp_1

https://drive.google.com/file/d/0BxKBnD5y2M8NREZod0tVdW5FLTQ/view

http://blog.yhat.com/posts/python-random-forest.html RANDOM FOREST

https://jeremykun.com/2016/02/08/big-dimensions-and-what-you-can-do-about-it/

https://jeremykun.com/2016/04/18/singular-value-decomposition-part-1-perspectives-on-linear-algebra/

https://habrahabr.ru/company/npl/blog/311812/

https://habrahabr.ru/company/wunderfund/blog/311598/

https://mostafa-samir.github.io/ml-theory-pt1/

https://mostafa-samir.github.io/ml-theory-pt2/

https://mostafa-samir.github.io/ml-theory-pt3/

http://sebastianraschka.com/blog/2016/model-evaluation-selection-part1.html

http://sebastianraschka.com/blog/2016/model-evaluation-selection-part2.html

http://sebastianraschka.com/blog/2016/model-evaluation-selection-part3.html

http://bayesjumping.net/

https://sites.google.com/site/mikelubinsky/ml101

https://sites.google.com/site/mikelubinsky/ml-1

https://sites.google.com/site/mikelubinsky/ml/machinelearning

https://github.com/DistrictDataLabs/machine-learning

https://github.com/ianozsvald/data_science_delivered

https://github.com/savarin/pyconuk-introtutorial

http://www.kdnuggets.com/2016/09/great-algorithm-tutorial-roundup.html

https://indico.io/blog/simple-practical-path-to-machine-learning-capability-part3/

https://news.ycombinator.com/item?id=12557212

https://www.analyticsvidhya.com/blog/2016/09/40-interview-questions-asked-at-startups-in-machine-learning-data-science

https://github.com/thundergolfer

https://backchannel.com/an-exclusive-look-at-how-ai-and-machine-learning-work-at-apple-8dbfb131932b#.ve1hrp1oa

https://news.ycombinator.com/item?id=12352587

https://medium.com/learning-new-stuff/machine-learning-in-a-year-cdb0b0ebd29c#.ougi3oxoc

CLASSES

https://yandexdataschool.ru/

https://see.stanford.edu/Course/CS229

http://www.dataschool.io/machine-learning-with-scikit-learn/

https://www.edx.org/xseries/data-science-engineering-apache-spark

https://www.reddit.com/r/MachineLearning/comments/51qhc8/phdlevel_courses/

https://sookocheff.com/post/datascience/datasciencespecialization/

http://online.cambridgecoding.com/notebooks/cca_admin/deep-learning-for-complete-beginners-recognising-handwritten-digits

All models are wrong, but some are useful — famed statistician George Box

machine-learning-cheat-sheet.pdf

http://heather.cs.ucdavis.edu/draftregclass.pdf

http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/ BOOK

• Deep Learning: Deep Learning (Udacity), Neural Networks for Machine Learning (Coursera)

• Spark: Big Data Analysis with Spark (edX), Distributed Machine Learning with Spark (edX)

http://blog.yhat.com/posts/ML-resources-you-should-know.html

http://importknowledge.com/ daily news

https://sites.google.com/site/mikelubinsky/ml-1

http://blog.kaggle.com/2016/07/21/approaching-almost-any-machine-learning-problem-abhishek-thakur/

https://github.com/ben519/MLPB ML problems bundle

https://gab41.lab41.org/the-10-algorithms-machine-learning-engineers-need-to-know-f4bb63f5b2fa#.b3hufdxwq

http://importknowledge.com/your-first-machine-learning-project-in-python-step-by-step/

https://arogozhnikov.github.io/2016/04/28/demonstrations-for-ml-courses.html

http://fastml.com/bayesian-machine-learning/

http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/

Reinforcement Learning

https://webdocs.cs.ualberta.ca/~sutton/book/ Sutton reinforcement learning 2nd edition

https://www.youtube.com/watch?v=2pWv7GOvuf0

https://github.com/yandexdataschool/Practical_RL/tree/master

http://www.wildml.com/2016/10/learning-reinforcement-learning/

https://arxiv.org/abs/1701.07274

http://cs.stanford.edu/people/karpathy/reinforcejs/

http://ciml.info/

https://github.com/HFTrader/DeepLearningBook

https://github.com/datasciencemasters/go

https://github.com/open-source-society/data-science

https://www.amazon.com/Introducing-Data-Science-Machine-Learning/dp/1633430030

https://github.com/rasbt/python-machine-learning-book

https://github.com/amueller/introduction_to_ml_with_python

https://news.ycombinator.com/item?id=11985709

https://habrahabr.ru/company/retailrocket/blog/302828/

HMM

https://habrahabr.ru/company/surfingbird/blog/176461/

http://stats.stackexchange.com/questions/165/how-would-you-explain-markov-chain-monte-carlo-mcmc-to-a-layperson

https://news.ycombinator.com/item?id=13750621

https://jeremykun.com/2015/04/06/markov-chain-monte-carlo-without-all-the-bullshit/

https://web.stanford.edu/~jurafsky/slp3/8.pdf

https://news.ycombinator.com/item?id=11908890

https://habrahabr.ru/post/241317/ Markov random field

https://openai.com/blog/generative-models/

http://ocw.mit.edu/courses/aeronautics-and-astronautics/16-410-principles-of-autonomy-and-decision-making-fall-2010/lecture-notes/MIT16_410F10_lec20.pdf

https://blog.dominodatalab.com/an-introduction-to-model-based-machine-learning/

http://michal.sustr.sk/blog/outlier-analysis/