Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
Machine-Learning
Awesome list (courses, books, videos etc.) and implementation of Machine Learning Algorithms
https://github.com/ElizaLo/Machine-Learning
- K Nearest Neighbor, K-NN
- Logistic Regression
- Linear Regression
- Polynomial Regression
- Spam Detection
- Text Generator
- Question Answering System using BiDAF Model on SQuAD
- machine-learning-map.png
- MIT OpenCourseWare
- Course website
- Machine Learning Interviews
- Course website
- :octocat: repo on github
- Course website
- UCL Course on Reinforcement Learning by David Silver
- Course website
- Course website
- Course website
- Course website
- Course website
- Course website
- Course Website
- Course Website
- Course Website
- Cognitive Class.ai
- DeepLearning.AI
- Top 40 COMPLETELY FREE Coursera Artificial Intelligence and Computer Science Courses
- 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
- Spesialization: Advanced Machine Learning
- Machine Learning
- Машинное обучение - obucheniye)
- Machine Learning Foundations: A Case Study Approach
- Practical Predictive Analytics: Models and Methods
- Calculus: Single Variable Part 1
- Calculus One
- Современная комбинаторика
- Теория вероятностей для начинающих
- Линейная алгебра
- Эконометрика - недельный курс от ВШЭ
- Customer Analytics
- Social Network Analysis
- Social and Economic Networks: Models and Analysis
- Introduction to Recommender Systems
- Machine Learning
- Machine Learning Engineer Nanodegree
- Data Analyst Nanodegree
- Intro to Machine Learning - to-end process of investigating data through a machine learning lens
- Intro to Descriptive Statistics
- Data Science and Engineering with Spark
- Introduction to Computational Thinking and Data Science
- MITx: 6.041x Introduction to Probability - The Science of Uncertainty
- The Analytics Edge
- Intro to Python for Data Science
- stepic.org
- Python Knowledge Map
- Data Science
- Machine Learning Crash Course with TensorFlow APIs - Google's fast-paced, practical introduction to machine learning
- Google Cloud Training
- Google Developers Training
- Technical Writing Courses
- Qwiklabs
- Grow.Google
- LearnDigital.WithGoogle
- TensorFlow: Coding TensorFlow
- 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)
- - 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
- Machine Learning Notebooks
- AAAI, Association for the Advancement of Artificial Intelligence
- AAMAS, International Conference on Autonomous Agents and Multi-Agent Systems
- CVPR, IEEE Conference on Computer Vision and Pattern Recognition
- ICML, International Conference on Machine Learning
- ICCBR, International Conference on Case-Based Reasoning
- ICCV, International Computer Vision Conference
- IEEE, International Conference on Data Mining
- IJCAI, International Joint Conferences on Artificial Intelligence
- NIPS, NeurIPS, Conference on Neural Information Processing Systems
- SIAM, Society for Industrial and Applied Mathematics
- ICDM, IEEE International Conference on Data Mining
- MDS, Conference on Mathematics of Data Science
- SIGKDD, Conference on Knowledge Discovery and Data Mining
- O'Reilly AI Conference - O'Reilly Artificial Intelligence Conference
- ACL, Association for Computational Linguistics
- EMNLP, Empirical Methods in Natural Language Processing
- IJCNLP, International Joint Conference on Natural Language Processing
- Open Data Science Conference
- The Data Science Conference
- Strata Data & AI Conference
- useR!
- Deep Learning Summit
- MAIS, Montreal AI Symposium
- NAACLP, North American Chapter of the Association for Computational Linguistics
- ECML, European Conference on Machine Learning
- ECCV, European Conference on Computer Vision
- EACL, European Chapter of the Association for Computational Linguistics
- Data Science UA Conference
- AI Ukraine
- Data Science fwdays
- - Join 20K+ developers in learning how to responsibly deliver value with applied ML.
- Tutorials
- Top-down learning path: Machine Learning for Software Engineers
- 100-Days-Of-ML-Code
- ml-course-msu
- 100-best-github-machine-learning
- awesome-machine-learning
- trekhleb, homemade-machine-learning
- trekhleb, machine-learning-experiments
- trekhleb, machine-learning-octave
- Machine Learning Notebooks
- Open Source Society University's Data Science
- data-science-blogs
- Dive into Machine Learning - into-machine-learning)) with Python Jupyter notebook and scikit-learn|
- курса «Математика и Python» - recommendations.md)|
- Литература для поступления в ШАД
- 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
- - 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 awesomeness
- - 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
- - List of awesome university courses for learning Computer Science
- - Awesome free machine learning and AI courses with video lectures
- - A curated list of awesome Deep Learning tutorials, projects and communities
- - The most cited deep learning papers
- - 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)
- Code
- 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
- - 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
- - domain question answering;
- Reinforcement Learning: An Introduction (2nd Edition)
- David Silver's Reinforcement Learning Course (UCL, 2015)
- Introduction to Reinforcement learning with David Silver (DeepMind)
- Course website
- CS294 - Deep Reinforcement Learning (Berkeley, Fall 2015)
- CS 8803 - Reinforcement Learning (Georgia Tech)
- CS885 - Reinforcement Learning (UWaterloo), Spring 2018
- CS294-112 - Deep Reinforcement Learning (UC Berkeley)
- 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)
- Tutorial: Introduction to Reinforcement Learning with Function Approximation
- John Schulman - Deep Reinforcement Learning (4 Lectures)
- Deep Reinforcement Learning Slides @ NIPS 2016
- OpenAI Spinning Up
- Advanced Deep Learning & Reinforcement Learning (UCL 2018, DeepMind) -Deep RL Bootcamp
- dennybritz/reinforcement-learning
- - T. Hastie, R. Tibshirani, J. Friedman
- - test, ANOVA, корреляция, регрессия и др.)
- Code
- 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
- awesome-causality-algorithms
- Causal Decision Trees
- Discovery of Causal Rules Using Partial Association
- Causal Inference in Data Science From Prediction to Causation
- Machine Learning System Design
- How to deploy Machine Learning models as a Microservice using FastAPI
- Почему Вы должны попробовать FastAPI?
- - into-machine-learning)) with Python Jupyter notebook and scikit-learn
- - Directory of Python books
- - руководство по нюансам языка, мимо которых часто проходят новички (автор — Yasoob Khalid);
- - notebook на русском языке
- - learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
- - серьезная IDE для больших проектов
- - 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
- - - Style Guide for Python Code.
- - side scripting
- - книга для тех, кто хочет повысить свой навык программирования на R и лучше понять этот язык (в т.ч. для программистов на других языках);
- - org/mlr)) — Interface to a large number of classification and regression techniques, including machine-readable parameter descriptions;
- http://www.texstudio.org/
- https://www.lyx.org/
- Kaggle
- Google’s Public Data Sets
- /r/datasets
- UCI Machine Learning Repository
- awesome-public-datasets - quality open datasets in public domains (on-going)
- Early stopping
- Code
- Subset of MNIST
- Math
- "Batch" Gradient Descent
- Gradient Descent For Linear Regression
- Gradient Descent For Multiple Variables
- Polynomial Regression
- Code
- Code
- MNIST Database
- Code
- Code
- Understanding GRU Networks
- Code
- SMS Spam Collection Dataset
- Code
- Base text - **Alice in Wonderland**
- 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).
- Project Repository
- Paper
- Used Articles
- Useful Articles
- Useful Links
Keywords
machine-learning
31
deep-learning
21
python
20
data-science
17
awesome
13
awesome-list
12
neural-network
8
artificial-intelligence
7
nlp
7
machine-learning-algorithms
6
scikit-learn
5
data-mining
5
tensorflow
5
natural-language-processing
4
statistics
4
data-analysis
4
datascience
4
pytorch
4
linear-algebra
4
jupyter-notebook
4
gradient-boosting
3
numpy
3
mathematics
3
reinforcement-learning
3
random-forest
3
data-visualization
3
logistic-regression
3
neural-networks
3
classifier
3
tutorial
3
deeplearning
3
aws
2
decision-tree
2
ml
2
book
2
classification-trees
2
recommender-system
2
catboost
2
gradient-boosting-machine
2
lightgbm
2
computer-vision
2
algorithms
2
linear-regression
2
xgboost
2
support-vector-machines
2
big-data
2
jupyter
2
machinelearning
2
ai
2
clustering
2