Machine-Learning
Awesome list (courses, books, videos etc.) and implementation of Machine Learning Algorithms
https://github.com/ElizaLo/Machine-Learning
Last synced: 5 days ago
JSON representation
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📚 Books
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Boris Mirkin
- - 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)
- - Jiawei Han et. al.
- - Peter Harrington
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Carl E. Rasmussen, Christopher K. I. Williams
- - P. Flach (2012)
- - C.M.Bishop (2006)
- - перевод [Mining Massive Datasets](http://www.mmds.org/) - Jure Leskovec, Anand Rajaraman, Jeff Ullman
- - Richard S. Sutton, Andrew G. Barto
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Tom Mitchell
- - Hal Daumé III ([another link](http://ciml.info))
- - M.J.Zaki, W.Meira Jr (2014)
- - Charu C Aggarwal, Jiawei Han (eds.)
- - Nada Lavrac, Saso Dzeroski
- - Nils J Nilsson (1997)
- - D. Michie, D. J. Spiegelhalter
- - 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
- - P. Flach (2012)
- - C.M.Bishop (2006)
- - Richard S. Sutton, Andrew G. Barto
- - перевод [Mining Massive Datasets](http://www.mmds.org/) - Jure Leskovec, Anand Rajaraman, Jeff Ullman
- Machine Learning Notebooks
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🔹 Online Courses
- Бесплатные курсы для студентов
- Эконометрика - недельный курс от ВШЭ
- Customer Analytics
- Data Analyst Nanodegree
- Coursera Together: Free online learning during COVID-19
- Intro to Machine Learning - to-end process of investigating data through a machine learning lens
- Data Science and Engineering with Spark
- Get Started with Data Science Foundations
- Machine Learning
- Data Science
- Бесплатные курсы для изучения навыков в области облачных технологий
- Machine Learning Foundations: A Case Study Approach
- DeepLearning.AI
- Эконометрика - недельный курс от ВШЭ
- Practical Predictive Analytics: Models and Methods
- LearnDigital.WithGoogle
- Grow.Google
- Top 40 COMPLETELY FREE Coursera Artificial Intelligence and Computer Science Courses
- Calculus One
- Машинное обучение - obucheniye)
- Calculus: Single Variable Part 1
- Introduction to Recommender Systems
- Machine Learning
- Machine Learning Engineer Nanodegree
- Python Knowledge Map
- Machine Learning Crash Course with TensorFlow APIs - Google's fast-paced, practical introduction to machine learning
- Technical Writing Courses
- Cognitive Class.ai
- DeepLearning.AI
- Бесплатные курсы для изучения навыков в области облачных технологий
- Бесплатные курсы для студентов
- Coursera Together: Free online learning during COVID-19
- Get Started with Data Science Foundations
- Practical Predictive Analytics: Models and Methods
- Introduction to Computational Thinking and Data Science
- MITx: 6.041x Introduction to Probability - The Science of Uncertainty
- The Analytics Edge
- stepic.org
- Google Developers Training
- LearnDigital.WithGoogle
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Code editors
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Ukraine
- SublimeREPL - запускает `Read-eval-print loop` в соседней вкладке, удобно для пошаговой отладки кода
- - 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
- Auto-PEP8 - приводит код в соответствие с каноном стиля *pep8*
- Python Checker - проверка кода
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Big Data
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Ukraine
- Data Engineer VS Data Scientist
- Data Engineer VS Data Scientist
- 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 Scientist vs Data Engineer
- Data Engineer VS Data Scientist
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🎓 Courses
- Course website
- Course website
- Machine Learning Interviews
- :octocat: repo on github
- Course website
- Course website
- Course Website
- Course website
- Course website
- Course website
- Course website
- Course website
- UCL Course on Reinforcement Learning by David Silver
- Course website
- Course Website
- Course Website
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Table of Contents
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Machine Learning Map
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Reinforcement Learning
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Ukraine
- David Silver's Reinforcement Learning Course (UCL, 2015)
- Reinforcement Learning: An Introduction (2nd Edition)
- Introduction to Reinforcement Learning (Joelle Pineau @ Deep Learning Summer School 2016)
- Course website
- CS885 - Reinforcement Learning (UWaterloo), Spring 2018
- Deep Reinforcement Learning (Pieter Abbeel @ Deep Learning Summer School 2016)
- Deep Reinforcement Learning ICML 2016 Tutorial (David Silver)
- Tutorial: Introduction to Reinforcement Learning with Function Approximation
- John Schulman - Deep Reinforcement Learning (4 Lectures)
- CS294-112 - Deep Reinforcement Learning (UC Berkeley)
- Deep Reinforcement Learning Slides @ NIPS 2016
- OpenAI Spinning Up
- CS 8803 - Reinforcement Learning (Georgia Tech)
- Introduction to Reinforcement Learning (Joelle Pineau @ Deep Learning Summer School 2016)
- Deep Reinforcement Learning (Pieter Abbeel @ Deep Learning Summer School 2016)
- Deep Reinforcement Learning ICML 2016 Tutorial (David Silver)
- John Schulman - Deep Reinforcement Learning (4 Lectures)
- dennybritz/reinforcement-learning
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Linear Algebra
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Theory of Probability and Mathematical Statistics
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Neural Networks
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Ukraine
- - то иначе охарактеризовать Джеффри Хинтона (человека, стоящего у истоков современных подходов к обучению нейросетей с помощью алгоритма обратного распространения ошибки) сложно. Курс у него получился отличный»
- - книга по нейросетям и глубинному обучению ([:octocat: repo on github](https://github.com/mnielsen/neural-networks-and-deep-learning))
- - week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision
- - книга по нейросетям и глубинному обучению ([:octocat: repo on github](https://github.com/mnielsen/neural-networks-and-deep-learning))
- - week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision
- - domain question answering;
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LaTeX
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Ukraine
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🟥 YouTube
- Google Cloud Platform: AI Adventures
- Lviv Data Science Summer School 2020 lectures
- Samsung AI Innovation Campus - Russia
- Machine Learning University - samples/aws-machine-learning-university-accelerated-nlp)
- Google Cloud Platform: AI Adventures
- 3Blue1Brown
- Grammarly AI-NLP Club
- Lviv Data Science Summer School 2020 lectures
- Samsung AI Innovation Campus - Russia
- Machine Learning University - samples/aws-machine-learning-university-accelerated-nlp)
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R
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Conferences
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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
- 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!
- MDS, Conference on Mathematics of Data Science
- AAMAS, International Conference on Autonomous Agents and Multi-Agent Systems
- CVPR, IEEE Conference on Computer Vision and Pattern Recognition
- ICCBR, International Conference on Case-Based Reasoning
- IEEE, International Conference on Data Mining
- SIAM, Society for Industrial and Applied Mathematics
- Open Data Science Conference
- The Data Science Conference
- useR!
- Deep Learning Summit
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North America
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Europe
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Ukraine
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▶️ Websites
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Ukraine
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:octocat: GitHub Repositories
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Ukraine
- 100-best-github-machine-learning
- Machine learning cheat sheet - soulmachine (2015)|
- курса «Математика и Python» - recommendations.md)|
- Литература для поступления в ШАД
- Top-down learning path: Machine Learning for Software Engineers
- 100-Days-Of-ML-Code
- ml-course-msu
- 100-best-github-machine-learning
- trekhleb, homemade-machine-learning
- trekhleb, machine-learning-experiments
- trekhleb, machine-learning-octave
- Machine Learning Notebooks
- data-science-blogs
- Dive into Machine Learning - into-machine-learning)) with Python Jupyter notebook and scikit-learn|
- Machine learning cheat sheet - soulmachine (2015)|
- Probabilistic Programming and Bayesian Methods for Hackers
- ml-surveys
- Machine_Learning_and_Deep_Learning
- MachineLearning_DeepLearning
- Machine Learning Guide
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Awesome List
-
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
- - Probably the best curated list of data science software in Python
- - Curated list of Python resources for data science
- - quality open datasets in public domains (on-going)
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📌 Other
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Ukraine
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Deploy Machine Learning Model to Production
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Ukraine
- How to deploy Machine Learning models as a Microservice using FastAPI
- How to deploy Machine Learning models as a Microservice using FastAPI
- Почему Вы должны попробовать FastAPI?
- How to deploy Machine Learning models as a Microservice using FastAPI
- How to deploy Machine Learning models as a Microservice using FastAPI
- How to deploy Machine Learning models as a Microservice using FastAPI
- Почему Вы должны попробовать FastAPI?
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What's is the difference between _train, validation and test set_, in neural networks?
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Ukraine
- Understanding GRU Networks
- Code
- Early stopping
- Code
- Base text - **Alice in Wonderland**
- Code
- Code
- Code
- Understanding GRU Networks
- Code
- SMS Spam Collection Dataset
- Formatted text of **Alice in Wonderland**
- implementation - character inputs as described in the original paper and improving [GauthierDmns' code](https://github.com/GauthierDmn/question_answering).
- Paper
- Used Articles
- Understanding GRU Networks
- Understanding GRU Networks
- MNIST Database
- Subset of MNIST
- Code
- Understanding GRU Networks
- Code
- SMS Spam Collection Dataset
- Code
- Base text - **Alice in Wonderland**
- Formatted text of **Alice in Wonderland**
- Paper
- Used Articles
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Python, IPython, Scikit-learn etc.
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Ukraine
- - notebook на русском языке
- - into-machine-learning)) with Python Jupyter notebook and scikit-learn
- - руководство по нюансам языка, мимо которых часто проходят новички (автор — Yasoob Khalid);
- - learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
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Algorithms
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Machine Learning System Design
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Ukraine
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Causal Inference
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Ukraine
- Causality for Machine Learning, Bernhard Schölkopf, 2019
- 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
- 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
- Causal Decision Trees
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Bayesian Statistics
-
Ukraine
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📑 Open Datasets list
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Ukraine
-
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JavaScript-libraries for visualizing
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Reddit
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Social Networks (chanels, chats, groups, etc.)
Programming Languages
Categories
📚 Books
67
🔹 Online Courses
47
Awesome List
41
Theory of Probability and Mathematical Statistics
33
Conferences
32
🎓 Courses
29
What's is the difference between _train, validation and test set_, in neural networks?
28
Causal Inference
27
Python, IPython, Scikit-learn etc.
27
📌 Other
23
:octocat: GitHub Repositories
20
Reinforcement Learning
18
Big Data
15
Linear Algebra
14
Code editors
13
Neural Networks
12
🟥 YouTube
10
Reddit
9
LaTeX
8
R
8
Deploy Machine Learning Model to Production
7
Social Networks (chanels, chats, groups, etc.)
5
Table of Contents
5
Algorithms
4
▶️ Websites
4
JavaScript-libraries for visualizing
3
📑 Open Datasets list
2
Machine Learning Map
1
Machine Learning System Design
1
Bayesian Statistics
1
Sub Categories
Keywords
machine-learning
29
python
19
deep-learning
18
data-science
15
awesome
14
awesome-list
13
neural-network
8
artificial-intelligence
6
nlp
6
tensorflow
5
jupyter-notebook
5
data-mining
5
machine-learning-algorithms
5
scikit-learn
4
datascience
4
linear-algebra
4
reinforcement-learning
3
statistics
3
neural-networks
3
logistic-regression
3
deeplearning
3
data-visualization
3
numpy
3
data-analysis
3
pytorch
3
natural-language-processing
3
random-forest
3
tutorial
3
gradient-boosting
3
classifier
3
learning
2
xgboost
2
papers
2
lightgbm
2
gradient-boosting-machine
2
tutorials
2
clustering
2
list
2
deepwalk
2
decision-tree
2
jupyter
2
classification-trees
2
linear-regression
2
catboost
2
recommender-system
2
time-series
2
ml
2
node2vec
2
graph-classification
2
classification-algorithm
2