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Online learning is a common technique used in areas of machine learning where it is computationally infeasible to train over the entire dataset, requiring the need for out-of-core algorithms. It is also used in situations where it is necessary for the algorithm to dynamically adapt to new patterns in the data, or when the data itself is generated as a function of time, e.g., stock price prediction. Online learning algorithms may be prone to catastrophic interference, a problem that can be addressed by incremental learning approaches. Online Learning: https://youtu.be/3oOipgCbLIk River Website: https://riverml.xyz/dev/ Vowpal Wabbit: https://vowpalwabbit.org/ Code used: https://github.com/campusx-official/online-ml-sklearn-demo ============================ Do you want to learn from me? Check my affordable mentorship program at : https://learnwith.campusx.in/s/store ============================ 📱 Grow with us: CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789 Discord: https://discord.gg/PsWu8R87Z8 E-mail us at [email protected] ✨ Hashtags✨ #100DaysOfMachineLearning #MachineLearningFullCourse #MachineLearningInHindi ⌚Time Stamps⌚ 00:00 - Intro 00:42 - What is Online Machine Learning? 02:20 - How does online ML works? 05:58 - When should you use Online ML? 09:55 - River Library 10:45 - Vowpal Wabbit 11:20 - What is Online Learning Rate? 12:30 - Out of Core Learning 14:15 - Disadvantages of Online ML 16:50 - Differences between Batch vs Online Learning 19:10 - Outro
