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Machine learning books and papers

Machine learning books and papers

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📈 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 8 019 in the Education category and 13 748 in the Iran region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 24 509 subscribers.

According to the latest data from 04 July, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -101 over the last 30 days and by 3 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 6.50%. Within the first 24 hours after publication, content typically collects 2.21% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 594 views. Within the first day, a publication typically gains 541 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, مقاله, framework, graph.

📝 Description and content policy

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

Thanks to the high frequency of updates (latest data received on 05 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
+324 hours
-97 days
-10130 days
Posts Archive
Bayesian Reasoning and Machine Learning — D. Barber (en) 2012/2017. #book #beginner #theory @Machine_learn
Bayesian Reasoning and Machine Learning — D. Barber (en) 2012/2017. #book #beginner #theory @Machine_learn

#Frank Kane — Frank Kane's Taming Big Data with Apache Spark and Python (en) 2017 #book #python @Machine_learn

#Frank Kane — Frank Kane's Taming Big Data with Apache Spark and Python (en) 2017 #book #python @Machine_learn
#Frank Kane — Frank Kane's Taming Big Data with Apache Spark and Python (en) 2017 #book #python @Machine_learn

Data Science Fundamentals for Python and MongoDB — D. Paper (en) 2018 #book @Machine_learn

Data Science Fundamentals for Python and MongoDB — D. Paper (en) 2018 #book @Machine_learn
Data Science Fundamentals for Python and MongoDB — D. Paper (en) 2018 #book @Machine_learn

#Veracity of Big Data — V. Pendyala (en) 2018 #book #middle @Machine_learn

#Veracity of Big Data — V. Pendyala (en) 2018 #book #middle @Machine_learn
#Veracity of Big Data — V. Pendyala (en) 2018 #book #middle @Machine_learn

#Machinelearning for beginner s #page count=128 #Year=2017 #book @Machine_learn

#Machinelearning for beginner s #page count=128 #Year=2017 #book @Machine_learn
#Machinelearning for beginner s #page count=128 #Year=2017 #book @Machine_learn

#deeplearning #j.patterson and Adam #book #page count=523 @Machine_learn

#deeplearning #j.patterson and Adam #book #page count=523 @Machine_learn
#deeplearning #j.patterson and Adam #book #page count=523 @Machine_learn

# Smart Grid using Big Data Analytics: A #Random Matrix Theory Approach — R. C. Qiu, P. Antonik (en) 2017 #book @Machine_learn

#text analytics with python #book #Machine_learn

#Machine_learning and security #book @Machine_learn

#next-generation big data #big-data @Machine_learn

#Alice_Zheng_Feature_Engineering_for_Machine_Learning_2018 #book @Machine_learn

#Keras Deep Learning Cookbook: Over 80 Recipes for Implementing Deep Neural Networks in Python #book @Machine_learn

Python 3 Object oriented Programming #book @Machine_learn

#Deep learning with keras @Machine_learn