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Machine learning books and papers

Machine learning books and papers

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πŸ“ˆ Analytical overview of Telegram channel Machine learning books and papers

Channel Machine learning books and papers (@machine_learn) in the English language segment is an active participant. Currently, the community unites 24 509 subscribers, ranking 8 014 in the Education category and 13 742 in the Iran region.

πŸ“Š Audience metrics and dynamics

Since its creation on Π½Π΅Π²Ρ–Π΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 24 509 subscribers.

According to the latest data from 06 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -100 over the last 30 days and by 1 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 6.32%. Within the first 24 hours after publication, content typically collects 2.04% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 548 views. Within the first day, a publication typically gains 500 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 2.
  • Thematic interests: Content is focused on key topics such as disorder, psy, Ω…Ω‚Ψ§Ω„Ω‡, framework, graph.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€œAdmin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn”

Thanks to the high frequency of updates (latest data received on 07 July, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

24 509
Subscribers
+124 hours
+17 days
-10030 days
Posts Archive
Entropy Based Feature Selection.pdf8.64 KB

#Ensemble_methods - Zhou #book @Machine_learn

#machine_learning_for_audio,_image #book @Machine_learn

#learning_scikit_learn_machine_learning #book @Machine_learn

#python_power #book @Machine_learn

#Artificial Intelligence A Modern Approach #book @Machine_learn

#Python_for_Data_Analysis #book @Machine_learn

#Learning_Love Data Science @Machine_learn

#advance_in_computational_intelligence #paper @Machine_learn

#Towards a Multi-agent System for Medical Records Processing and Knowledge Discovery #paper @Machine_learn

#a_fast, unsupervised approach for medical concept extraction #paper @Machine_learn

#Intention_based_Information_Retrieval of_Electronic_Healt_Records #paper @Machine_learn

#Deep_Learning_in_Bioinformatics #paper @Machine_learn

#Deep_Learning_for_Medical #Image #Processing:Overview, Challenges and Future #paper @Machine_learn

#GMM #report @Machine_learn

#object_oriented_python #book @Machine_learn

#python3_Documentaino #book @Machine_learn

#DeepLearning #Unsupervised #Book @Machine_learn

#A_Multiclass_Classification_Method #paper @Machine_learn

#Proceedings_of_NEWS_2016_The_Sixth_Named_Entities_Workshop #book @Machine_learn