Answers.Body: <p>Although you don't seem to want to read papers, you should be able to follow the first pages of the following two papers, if you are familiar with the basics of machine learning (ML).</p>
  <ul>
  <li><p><a href="https://www.sciencedirect.com/science/article/pii/S0925231221006706" rel="nofollow noreferrer">Online learning: A comprehensive survey</a> (2019) by Steven C. H. Hoi et al.</p>
  </li>
  <li><p><a href="http://w3.cs.huji.ac.il/%7Eshais/papers/OLsurvey.pdf" rel="nofollow noreferrer">Online Learning and Online Convex Optimization</a> (2011), by Shai Shalev-Shwartz</p>
  </li>
  </ul>
  <p>So, after having read these initial pages, you should have an overview of what online learning is. After that, you can dive into the details, if you think that you have the knowledge to understand them. Otherwise, you should probably pick <a href="https://ai.stackexchange.com/q/23507/2444">an introductory ML book</a>.</p>
  
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Answers.Body: <h6><a href="https://datascience.stackexchange.com/questions/104187/resources-on-on-line-machine-learning/104489#104489">Duplicate of answer previously posted on DataScience.SE:</a></h6>
  <p>On-line learning algorithms trains new data as it arrives. It is often referred to as incremental learning or continuous learning as it trains continuous stream of data incrementally</p>
  <p>As requested some resources in the form of books, tutorial, lecture notes, YouTube links, pdf documents along with available packages that support online learning algorithms are mentioned</p>
  <p><strong>BOOKS</strong></p>
  <ul>
  <li><a href="https://link.springer.com/book/10.1007/BFb0029561" rel="nofollow noreferrer">Online Algorithms: The State of the Art</a></li>
  <li><a href="https://www.cs.huji.ac.il/%7Eshais/papers/OLsurvey.pdf" rel="nofollow noreferrer">Online learning and Online convex optimization</a></li>
  <li><a href="http://sbubeck.com/SurveyBCB12.pdf" rel="nofollow noreferrer">Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems</a></li>
  <li><a href="http://sbubeck.com/Bubeck15.pdf" rel="nofollow noreferrer">Convex Optimization: Algorithms and Complexity</a></li>
  <li><a href="https://sites.google.com/view/intro-oco/" rel="nofollow noreferrer">Introduction to  Online Convex Optimization</a></li>
  <li><a href="http://sbubeck.com/BubeckLectureNotes.pdf" rel="nofollow noreferrer">Introduction to Online Optimization</a></li>
  </ul>
  <p><strong>TUTORIAL</strong></p>
  <ul>
  <li><a href="https://dziganto.github.io/data%20science/online%20learning/python/scikit-learn/An-Introduction-To-Online-Machine-Learning/" rel="nofollow noreferrer">An Introduction To Online Machine Learning</a></li>
  <li><a href="https://www.iunera.com/kraken/fabric/simple-introduction-to-online-learning-in-machine-learning/" rel="nofollow noreferrer">A Simple Introduction to Online Machine Learning</a></li>
  <li><a href="https://analyticsindiamag.com/beginners-guide-to-online-machine-learning/" rel="nofollow noreferrer">Beginner’s Guide to Online Machine Learning</a></li>
  <li><a href="https://medium.com/value-stream-design/online-machine-learning-515556ff72c5" rel="nofollow noreferrer">what is online machine learning</a></li>
  <li><a href="https://en.m.wikipedia.org/wiki/Online_machine_learning" rel="nofollow noreferrer">Online Machine Learning Wikipedia</a></li>
  <li><a href="https://www.analyticsvidhya.com/blog/2015/01/introduction-online-machine-learning-simplified-2/" rel="nofollow noreferrer">Online learning simplified</a></li>
  </ul>
  <p><strong>LECTURE</strong></p>
  <ul>
  <li><a href="https://www.mit.edu/%7Erakhlin/6.883/" rel="nofollow noreferrer">Online Methods in Machine Learning
  Theory and Applications</a></li>
  <li><a href="https://www.coursera.org/lecture/machine-learning/online-learning-ABO2q" rel="nofollow noreferrer">Coursera lecture on online machine learning</a></li>
  </ul>
  <p><strong>YOU TUBE</strong></p>
  <ul>
  <li><a href="https://m.youtube.com/watch?v=O3gd6elZOlA" rel="nofollow noreferrer">Introduction to online machine learning algorithms </a></li>
  <li><a href="https://youtu.be/V4VEJ6O5ycE" rel="nofollow noreferrer">what is Online Machine learning </a></li>
  <li><a href="https://m.youtube.com/watch?v=P3M6dt7bY9U" rel="nofollow noreferrer">Online Machine learning with creme</a></li>
  </ul>
  <p><strong>PDF</strong></p>
  <ul>
  <li><p><a href="https://home.ttic.edu/%7Eshai/papers/ShalevThesis07.pdf" rel="nofollow noreferrer">Online Learning:
  Theory, Algorithms, and Applications</a></p>
  </li>
  <li><p><a href="https://arxiv.org/abs/1802.02871" rel="nofollow noreferrer">Online Learning: A Comprehensive Survey</a></p>
  </li>
  <li><p><a href="https://www.cs.cmu.edu/%7Eavrim/Papers/survey.pdf" rel="nofollow noreferrer">14 chapter on-line learning algorithms in machine learning </a></p>
  </li>
  <li><p><a href="https://ieeexplore.ieee.org/document/6661782" rel="nofollow noreferrer">Utilisation of on-line machine learning for SCADA system alarms forecasting</a></p>
  </li>
  <li><p><a href="https://trace.tennessee.edu/cgi/viewcontent.cgi?article=6803&amp;context=utk_gradthes" rel="nofollow noreferrer">Online Machine Learning Algorithms Review and Comparison in Healthcare</a></p>
  </li>
  </ul>
  <p><strong>ONLINE LEARNING ALGORITHMS</strong></p>
  <ul>
  <li><p><a href="https://vowpalwabbit.org" rel="nofollow noreferrer">Vowpal Wabbit </a>: provides support for reinforcement learning and supervised learning</p>
  </li>
  <li><p><a href="https://github.com/MaxHalford/creme" rel="nofollow noreferrer">Creme</a></p>
  </li>
  <li><p><a href="https://github.com/scikit-multiflow/scikit-multiflow" rel="nofollow noreferrer">Scikit-multiflow</a></p>
  </li>
  <li><p><a href="https://github.com/online-ml/river" rel="nofollow noreferrer">River</a> - online machine learning python package that combines python packages creme and scikit-multiflow</p>
  </li>
  <li><p><a href="https://github.com/MaxHalford/creme" rel="nofollow noreferrer">LIBOL: A Library for Online Learning Algorithms</a></p>
  </li>
  <li><p>Quasi-additive Online Classification Algorithms</p>
  </li>
  <li><p><a href="https://github.com/scikit-multiflow/scikit-multiflow" rel="nofollow noreferrer">Game Theory, on-line prediction and boosting</a> Freund and Schapire relates boosting algorithms to online learning(Adaboost)</p>
  </li>
  </ul>
  
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Answers.Text: Note that I didn't even know that your answer existed on another site. I didn't even know that this question had also been asked on Data Science. So, I didn't copy from your answer, so I didn't plagiarise you, as you're attempting to demonstrate (with your flagging). In addition to that, you're not the author of those papers. Even if I had read your answer on the other site (which did **not** happen, again!), my answer is completely different from yours. Plagiarism happens you copy and paste and don't give proper attribution, which is not the case at all.
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Answers.Text: By the way, you flagged my answer as plagiarism, while, in reality, it seems that you copied and pasted the sentence "It is called online machine learning because the ML programming must be connected to a network that provides a dynamic input stream." from [https://www.techopedia.com/definition/32818/online-machine-learning](https://www.techopedia.com/definition/32818/online-machine-learning). If you don't edit your answer to give proper attribution to the author of that sentence, this answer, as much as it can be useful, will be deleted, as plagiarism is not allowed here.
Answers.UserDisplayName: nbro
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Answers.Text: In addition to that, you also copied "Vowpal Wabbit provides fast, efficient, and flexible online machine learning techniques for reinforcement learning, supervised learning, and more" from https://vowpalwabbit.org/
Answers.UserDisplayName: nbro
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-12-26T20:59:11.193Z
Answers.Text: This answer was partially copied from sources without attribution, so [this is considered plagiarism (which is forbidden and subject to deletion)](https://ai.stackexchange.com/help/referencing), so I deleted it. To fix that and if you think your answer should be undeleted, remove the copied parts and vote to undelete it.
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Answers.UserDisplayName: Archana David
Body: <p>Are there any resources (either books, articles, or tutorials) that introduce the basics of online machine learning?</p>
  <p>For example, <a href="https://web.eecs.umich.edu/%7Ejabernet/eecs598course/fall2015/web/" rel="nofollow noreferrer">this website</a> has nice lecture notes (from lec16) on some of the aspects. Or <a href="https://ii.uni.wroc.pl/%7Elukstafi/pmwiki/uploads/AGT/Prediction_Learning_and_Games.pdf" rel="nofollow noreferrer">this book</a>.</p>
  <p>I can't seem to find many resources on this. I'm trying to understand the basics, not read research papers.</p>
  
Tags: <machine-learning><reference-request><online-learning>
Title: Are there any resources that introduce the basics of online machine learning?
UserDisplayName: Slim Shady
ThreadText: # Are there any resources that introduce the basics of online machine learning?
  
  (Asked by: username_0 on 2021-11-15)
  
  Are there any resources (either books, articles, or tutorials) that introduce the basics of online machine learning?
  
  For example, [this website](https://web.eecs.umich.edu/%7Ejabernet/eecs598course/fall2015/web/) has nice lecture notes (from lec16) on some of the aspects. Or [this book](https://ii.uni.wroc.pl/%7Elukstafi/pmwiki/uploads/AGT/Prediction_Learning_and_Games.pdf).
  
  I can't seem to find many resources on this. I'm trying to understand the basics, not read research papers.
  
  **Tags:** machine-learning, reference-request, online-learning
  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2021-12-26. Score: 2 | Accepted answer
  
  Although you don't seem to want to read papers, you should be able to follow the first pages of the following two papers, if you are familiar with the basics of machine learning (ML).
  
  * [Online learning: A comprehensive survey](https://www.sciencedirect.com/science/article/pii/S0925231221006706) (2019) by Steven C. H. Hoi et al.
  * [Online Learning and Online Convex Optimization](http://w3.cs.huji.ac.il/%7Eshais/papers/OLsurvey.pdf) (2011), by Shai Shalev-Shwartz
  
  So, after having read these initial pages, you should have an overview of what online learning is. After that, you can dive into the details, if you think that you have the knowledge to understand them. Otherwise, you should probably pick [an introductory ML book](https://ai.stackexchange.com/q/23507/2444).
  
  
  ---
  
  ### Answer by username_2 on 2021-12-26. Score: 1
  
  ###### [Duplicate of answer previously posted on DataScience.SE:](https://datascience.stackexchange.com/questions/104187/resources-on-on-line-machine-learning/104489#104489)
  
  On-line learning algorithms trains new data as it arrives. It is often referred to as incremental learning or continuous learning as it trains continuous stream of data incrementally
  
  As requested some resources in the form of books, tutorial, lecture notes, YouTube links, pdf documents along with available packages that support online learning algorithms are mentioned
  
  **BOOKS**
  
  * [Online Algorithms: The State of the Art](https://link.springer.com/book/10.1007/BFb0029561)
  * [Online learning and Online convex optimization](https://www.cs.huji.ac.il/%7Eshais/papers/OLsurvey.pdf)
  * [Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems](http://sbubeck.com/SurveyBCB12.pdf)
  * [Convex Optimization: Algorithms and Complexity](http://sbubeck.com/Bubeck15.pdf)
  * [Introduction to  Online Convex Optimization](https://sites.google.com/view/intro-oco/)
  * [Introduction to Online Optimization](http://sbubeck.com/BubeckLectureNotes.pdf)
  
  **TUTORIAL**
  
  * [An Introduction To Online Machine Learning](https://dziganto.github.io/data%20science/online%20learning/python/scikit-learn/An-Introduction-To-Online-Machine-Learning/)
  * [A Simple Introduction to Online Machine Learning](https://www.iunera.com/kraken/fabric/simple-introduction-to-online-learning-in-machine-learning/)
  * [Beginner’s Guide to Online Machine Learning](https://analyticsindiamag.com/beginners-guide-to-online-machine-learning/)
  * [what is online machine learning](https://medium.com/value-stream-design/online-machine-learning-515556ff72c5)
  * [Online Machine Learning Wikipedia](https://en.m.wikipedia.org/wiki/Online_machine_learning)
  * [Online learning simplified](https://www.analyticsvidhya.com/blog/2015/01/introduction-online-machine-learning-simplified-2/)
  
  **LECTURE**
  
  * [Online Methods in Machine Learning
    Theory and Applications](https://www.mit.edu/%7Erakhlin/6.883/)
  * [Coursera lecture on online machine learning](https://www.coursera.org/lecture/machine-learning/online-learning-ABO2q)
  
  **YOU TUBE**
  
  * [Introduction to online machine learning algorithms](https://m.youtube.com/watch?v=O3gd6elZOlA)
  * [what is Online Machine learning](https://youtu.be/V4VEJ6O5ycE)
  * [Online Machine learning with creme](https://m.youtube.com/watch?v=P3M6dt7bY9U)
  
  **PDF**
  
  * [Online Learning:
    Theory, Algorithms, and Applications](https://home.ttic.edu/%7Eshai/papers/ShalevThesis07.pdf)
  * [Online Learning: A Comprehensive Survey](https://arxiv.org/abs/1802.02871)
  * [14 chapter on-line learning algorithms in machine learning](https://www.cs.cmu.edu/%7Eavrim/Papers/survey.pdf)
  * [Utilisation of on-line machine learning for SCADA system alarms forecasting](https://ieeexplore.ieee.org/document/6661782)
  * [Online Machine Learning Algorithms Review and Comparison in Healthcare](https://trace.tennessee.edu/cgi/viewcontent.cgi?article=6803&context=utk_gradthes)
  
  **ONLINE LEARNING ALGORITHMS**
  
  * [Vowpal Wabbit](https://vowpalwabbit.org) : provides support for reinforcement learning and supervised learning
  * [Creme](https://github.com/MaxHalford/creme)
  * [Scikit-multiflow](https://github.com/scikit-multiflow/scikit-multiflow)
  * [River](https://github.com/online-ml/river) - online machine learning python package that combines python packages creme and scikit-multiflow
  * [LIBOL: A Library for Online Learning Algorithms](https://github.com/MaxHalford/creme)
  * Quasi-additive Online Classification Algorithms
  * [Game Theory, on-line prediction and boosting](https://github.com/scikit-multiflow/scikit-multiflow) Freund and Schapire relates boosting algorithms to online learning(Adaboost)
  
  > **Comments:**
  > **username_1**: Note that I didn't even know that your answer existed on another site. I didn't even know that this question had also been asked on Data Science. So, I didn't copy from your answer, so I didn't plagiarise you, as you're attempting to demonstrate (with your flagging). In addition to that, you're not the author of those papers. Even if I had read your answer on the other site (which did **not** happen, again!), my answer is completely different from yours. Plagiarism happens you copy and paste and don't give proper attribution, which is not the case at all.  
  > **username_1**: By the way, you flagged my answer as plagiarism, while, in reality, it seems that you copied and pasted the sentence "It is called online machine learning because the ML programming must be connected to a network that provides a dynamic input stream." from [https://www.techopedia.com/definition/32818/online-machine-learning](https://www.techopedia.com/definition/32818/online-machine-learning). If you don't edit your answer to give proper attribution to the author of that sentence, this answer, as much as it can be useful, will be deleted, as plagiarism is not allowed here.  
  > **username_1**: In addition to that, you also copied "Vowpal Wabbit provides fast, efficient, and flexible online machine learning techniques for reinforcement learning, supervised learning, and more" from https://vowpalwabbit.org/  
  > **username_1**: This answer was partially copied from sources without attribution, so [this is considered plagiarism (which is forbidden and subject to deletion)](https://ai.stackexchange.com/help/referencing), so I deleted it. To fix that and if you think your answer should be undeleted, remove the copied parts and vote to undelete it.  
  
  ---
  
  