Машинное обучение
Постоянно обновляемая подборка ресурсов по машинному обучению.
Оглавление
- Календарь соревнований по анализу данных
- Машинное обучение: вводная лекция – К. В. Воронцов
- Lecture notes and code for Machine Learning practical course on CMC MSU
- 100+ Free Data Science Books – более 100 бесплатных книг по Data Science
- Free O'Reilly data science ebooks
- 100 репозиториев по машинному обучению
- awesome-machine-learning — A curated list of awesome Machine Learning frameworks, libraries and software
- Open Source Society University's Data Science course – this is a solid path for those of you who want to complete a Data Science course on your own time, for free, with courses from the best universities in the World
- Доска по data science в Trello — проверенные материалы, организованные по темам (expertise tracks, языки программирования, различные инструменты)
- Machine Learning Resource Guide
- 17 ресурсов по машинному обучению от Типичного Программиста
- 51 toy data problem in Data Science
- practical-pandas-projects — project ideas for improving one's Python data analysis skills
- Dive into Machine Learning
- Data Science Interview Questions — огромный список вопросов для подготовки к интервью на позицию data scientist'а
- Много книг по Natural Language Processing
- Список открытых источников данных, на которых можно найти бесплатные датасеты
- What should I learn in data science in 100 hours?
- machine-learning-for-software-engineers — A complete daily plan for studying to become a machine learning engineer
- Tutorials on topics in machine learning
- Постоянно обновляющаяся подборка ссылок по датасаенсу
- Teach yourself Machine Learning the hard way!
- An article a week – list of good articles on ML/AI/DL
- The most popular programming books ever mentioned on StackOverflow
- Cookiecutter Data Science – A logical, reasonably standardized, but flexible project structure for doing and sharing data science work
- awesome-datascience-ideas – A list of awesome and proven data science use cases and applications
- machine-learning-surveys – A curated list of Machine Learning Surveys, Tutorials and Books
- A hands-on data science crash course in Python by Bart De Vylder and Pieter Buteneers from CoScale
- docker-setup – A Curated List of Docker Images for Data Science Projects, with Easy Setup
- Notes on Artificial Intelligence – конспекты по разным ML-related темам, от алгебры до Байеса
Библиотека ML-специалиста
- A Course in Machine Learning – Hal Daumé III
- A Probabilistic Theory of Pattern Recognition – Devroye, Gyorfi, Lugosi (pdf)
- Applied Predictive Modeling – M. Kuhn, K. Johnson (2013)
- Bayesian Reasoning and Machine Learning - D.Barber (2015) (pdf)
- Core Concepts in Data Analysis: Summarization, Correlation and Visualization – Boris Mirkin
- Data Mining and Analysis. Fundamental Concepts and Algorithms – M.J.Zaki, W.Meira Jr (2014) (pdf)
- Data Mining: Concepts and Techniques – Jiawei Han et. al.
- Data Science For Dummies – Lillian Pierson (2015)
- Doing Data Science
- Elements of Statistical Learning – Hastie, Tibshirani, Friedman (pdf)
- Foundations of Machine Learning – Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar (2012)
- Frequent Pattern Mining – Charu C Aggarwal, Jiawei Han (eds.) (pdf)
- Gaussian Processes for Machine Learning – Carl E. Rasmugit lssen, Christopher K. I. Williams (pdf)
- Inductive Logic Programming: Techniques and Applications – Nada Lavrac, Saso Dzeroski
- Information Theory, Inference and Learning Algorithms – David MacKay
- Introduction to Information Retrieval – Manning, Rhagavan, Shutze (pdf)
- Introduction To Machine Learning – Nils J Nilsson (1997)
- Introduction to Machine Learning – Smola and Vishwanathan (pdf)
- Machine learning cheat sheet – soulmachine (2017) (pdf)
- Machine Learning in Action – Peter Harrington
- Machine Learning, Neural and Statistical Classification – D. Michie, D. J. Spiegelhalter
- Machine Learning. The Art of Science of Algorithms that Make Sense of Data – P. Flach (2012)
- Machine Learning – Tom Mitchell
- Machine Learning – Andrew Ng
- Mining Massive Datasets – Jure Leskovec, Anand Rajaraman, Jeff Ullman
- Pattern Recognition and Machine Learning – C.M.Bishop (2006)
- Probabilistic Programming and Bayesian Methods for Hackers (free)
- A Programmer's Guide to Data Mining – Ron Zacharski (pdf)
- R in Action
- Reinforcement Learning: An Introduction - Richard S. Sutton, Andrew G. Barto
- The LION Way Machine Learning plus Intelligent Optimization (pdf)
- Understanding Machine Learning: From Theory to Algorithms
- Анализ больших наборов данных – перевод Mining Massive Datasets
- Математические методы обучения по прецедентам (теория обучения машин) – К. В. Воронцов (pdf)
- Машинное обучение — Петер Флах (pdf)
- Методы ансамблирования обучающихся алгоритмов — диссертация А. Гущина (pdf)
Онлайн-курсы (MOOC)
Social
Обсуждение машинного обучения в мессенджерах (группы, каналы, чаты, сообщества).