Machine-Learning
Awesome list (courses, books, videos etc.) and implementation of Machine Learning Algorithms
https://github.com/ElizaLo/Machine-Learning
Last synced: 2 days ago
JSON representation
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Algorithms
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Awesome List
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Ukraine
- - A curated list of awesome awesomeness
- - A curated list of awesome Deep Learning tutorials, projects and communities
- - The most cited deep learning papers
- - A curated list of awesome adversarial machine learning resources
- - Curated list of reads, implementations and core concepts of Artificial Intelligence, Deep Learning, Machine Learning by best folk in the world
- - awesome tools and libs for AI, Deep Learning, Machine Learning, Computer Vision, Data Science, Data Analytics and Cognitive Computing that are baked in the oven to be Native on Kubernetes and Docker with Python, R, Scala, Java, C#, Go, Julia, C++ etc
- - A curated list of awesome big data frameworks, ressources and other awesomeness
- - A curated list of practical business machine learning (BML) and business data science (BDS) applications for Accounting, Customer, Employee, Legal, Management and Operations
- - Awesome free machine learning and AI courses with video lectures
- - A curated list of awesome Deep Learning for Natural Language Processing resources
- - A curated list of awesome Deep Reinforcement Learning resources
- - Papers on Graph neural network (GNN)
- - Directory of Python books
- - Curated list of Python resources for data science
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Bayesian Statistics
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Ukraine
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Big Data
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Ukraine
- Big Data Fundamentals: Concepts, Drivers & Techniques
- Big Data: Principles and best practices of scalable realtime data systems
- Big Data Specialization
- Data Scientist vs Data Engineer
- Data Engineer VS Data Scientist
- Data Engineer VS Data Scientist
- Data Engineer VS Data Scientist
- Data Engineer VS Data Scientist
- Data Engineer VS Data Scientist
- Data Engineer VS Data Scientist
- Data Engineer VS Data Scientist
- Data Engineer VS Data Scientist
- Data Engineer VS Data Scientist
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📚 Books
- - D.Barber (2015)
- - Boris Mirkin
- - Hal Daumé III ([another link](http://ciml.info))
- - Jiawei Han et. al.
- - M.J.Zaki, W.Meira Jr (2014)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Carl E. Rasmussen, Christopher K. I. Williams
- - Nada Lavrac, Saso Dzeroski
- - Nils J Nilsson (1997)
- - Tom Mitchell
- - D. Michie, D. J. Spiegelhalter
- - P. Flach (2012)
- - C.M.Bishop (2006)
- - Peter Harrington
- - Richard S. Sutton, Andrew G. Barto
- - перевод [Mining Massive Datasets](http://www.mmds.org/) - Jure Leskovec, Anand Rajaraman, Jeff Ullman
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - D.Barber (2015)
- - Hal Daumé III ([another link](http://ciml.info))
- - Jiawei Han et. al.
- - M.J.Zaki, W.Meira Jr (2014)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Carl E. Rasmussen, Christopher K. I. Williams
- - Nada Lavrac, Saso Dzeroski
- - Nils J Nilsson (1997)
- - D. Michie, D. J. Spiegelhalter
- - Richard S. Sutton, Andrew G. Barto
- Machine Learning Notebooks
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - P. Flach (2012)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - C.M.Bishop (2006)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Jiawei Han et. al.
- - Charu C Aggarwal, Jiawei Han (eds.)
- - перевод [Mining Massive Datasets](http://www.mmds.org/) - Jure Leskovec, Anand Rajaraman, Jeff Ullman
- - Charu C Aggarwal, Jiawei Han (eds.)
- - перевод [Mining Massive Datasets](http://www.mmds.org/) - Jure Leskovec, Anand Rajaraman, Jeff Ullman
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Causal Inference
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Ukraine
- A Crash Course in Causality: Inferring Causal Effects from Observational Data
- Powerful Concepts in Social Science
- MIT Statistics and Data Science Center, 2017
- Conference on Cognitive Computational Neuroscience 2019
- Machine Learning Summer School 2020
- Machine Learning Summer School 2013
- Causal Inference 3: Counterfactuals
- Causality for Machine Learning, Bernhard Schölkopf, 2019
- Elements of Causal Inference
- Causal Structure Learning,Christina Heinze-Deml, Marloes H. Maathuis, Nicolai Meinshausen, 2017
- Causal inference in statistics: An overview, 2009
- JUDEA PEARL, MADELYN GLYMOUR, NICHOLAS P. JEWELL CAUSAL INFERENCE IN STATISTICS: A PRIMER
- JUDEA PEARL - CAUSALITY, 2nd Edition, 2009
- Causation, Prediction, and Search, Second Edition
- Learning DAGs with Continuous Optimization
- Causality in cognitive neuroscience: concepts, challenges, and distributional robustness
- Active Invariant Causal Prediction: Experiment Selection through Stability, Juan L Gamella, Christina Heinze-Deml, 2020
- Investigating Causal Relations by Econometric Models and Cross-spectral Methods, 1969
- Fast Greedy Equivalence Search (FGES) Algorithm for Continuous Variables
- Greedy Fast Causal Inference (GFCI) Algorithm for Continuous Variables
- Causal Decision Trees
- Discovery of Causal Rules Using Partial Association
- Causal Inference in Data Science From Prediction to Causation
- Powerful Concepts in Social Science
- Machine Learning Summer School 2013
- Causation, Prediction, and Search, Second Edition
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Code editors
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Ukraine
- - VIM XXI века*;, отлично подходит для python, если использовать вместе с плагинами:
- Package Control - для быстрой и удобной работы с дополнениями
- Git - для работы с git
- Jedi - делает автодополнения для Python более умными и глубокими
- SublimeREPL - запускает `Read-eval-print loop` в соседней вкладке, удобно для пошаговой отладки кода
- Auto-PEP8 - приводит код в соответствие с каноном стиля *pep8*
- Python Checker - проверка кода
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Conferences
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Europe
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International
- AAMAS, International Conference on Autonomous Agents and Multi-Agent Systems
- ICCBR, International Conference on Case-Based Reasoning
- SIAM, Society for Industrial and Applied Mathematics
- MDS, Conference on Mathematics of Data Science
- SIGKDD, Conference on Knowledge Discovery and Data Mining
- ACL, Association for Computational Linguistics
- EMNLP, Empirical Methods in Natural Language Processing
- IJCNLP, International Joint Conference on Natural Language Processing
- The Data Science Conference
- Strata Data & AI Conference
- useR!
- ICCBR, International Conference on Case-Based Reasoning
- ICML, International Conference on Machine Learning
- Open Data Science Conference
- SIAM, Society for Industrial and Applied Mathematics
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North America
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Ukraine
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🎓 Courses
Programming Languages
Categories
📚 Books
76
🔹 Online Courses
42
Awesome List
40
Theory of Probability and Mathematical Statistics
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What's is the difference between _train, validation and test set_, in neural networks?
28
Causal Inference
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🎓 Courses
26
Conferences
23
📌 Other
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Python, IPython, Scikit-learn etc.
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:octocat: GitHub Repositories
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Reinforcement Learning
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Big Data
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Linear Algebra
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Code editors
12
Neural Networks
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🟥 YouTube
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Reddit
9
R
8
LaTeX
7
Social Networks (chanels, chats, groups, etc.)
5
Deploy Machine Learning Model to Production
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▶️ Websites
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JavaScript-libraries for visualizing
3
Table of Contents
3
📑 Open Datasets list
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Algorithms
3
Machine Learning Map
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Machine Learning System Design
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Bayesian Statistics
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Sub Categories
Keywords
machine-learning
30
python
20
deep-learning
19
data-science
16
awesome
14
awesome-list
13
neural-network
8
artificial-intelligence
7
machine-learning-algorithms
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nlp
6
jupyter-notebook
5
tensorflow
5
data-mining
5
scikit-learn
4
natural-language-processing
4
datascience
4
linear-algebra
4
numpy
3
pytorch
3
mathematics
3
neural-networks
3
tutorial
3
classifier
3
gradient-boosting
3
reinforcement-learning
3
random-forest
3
statistics
3
deeplearning
3
data-visualization
3
data-analysis
3
logistic-regression
3
algorithms
3
list
2
tutorials
2
jupyter
2
data-structures
2
ml
2
ai
2
graph-classification
2
coursera-machine-learning
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computer-science
2
time-series
2
classification-algorithm
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recommender-system
2
linear-regression
2
data-analytics
2
unsupervised-learning
2
catboost
2
node2vec
2
classification-trees
2