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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
Understanding Machine Learning from Theory to Algorithms – Shai Shalev-Shwartz, Shai Ben-David (en) 2014 #book #junior #theor
Understanding Machine Learning from Theory to Algorithms – Shai Shalev-Shwartz, Shai Ben-David (en) 2014 #book #junior #theory @Machine_learn

Gaussian Processes for Machine Learning – C. E. Rasmussen, Christopher K. I. Williams (en) 2006 #book #middle #theory @Machine_learn

Gaussian Processes for Machine Learning – C. E. Rasmussen, Christopher K. I. Williams (en) 2006 #book #middle #theory @Machin
Gaussian Processes for Machine Learning – C. E. Rasmussen, Christopher K. I. Williams (en) 2006 #book #middle #theory @Machine_learn

Advanced Analytics with Spark — S. Ryza и др. (en) 2017 #book #middle #spark @Machine_learn

Advanced Analytics with Spark — S. Ryza и др. (en) 2017 #book #middle #spark @Machine_learn
Advanced Analytics with Spark — S. Ryza и др. (en) 2017 #book #middle #spark @Machine_learn

#Alice_Zheng_Feature_Engineering_for_Machine_Learning_2018 #book @Machine_learn

#Alice_Zheng_Feature_Engineering_for_Machine_Learning_2018 #book @Machine_learn
#Alice_Zheng_Feature_Engineering_for_Machine_Learning_2018 #book @Machine_learn

discriminative : 1:#Regression 2:#Logistic regression 3:#decision tree(Hunt) 4:#neural network(traditional network, deep network) 5:#Support Vector Machine(SVM) Generative: 1:#Hidden Markov model 2:#Naive bayes 3:#K-nearest neighbor(KNN) 4:#Generative adversarial networks(GANs) Deep learning: 1:CNN 2:RNN 3:LSTM 4:CapsuleNet 5:Siamese: siamese cnn siamese lstm siamese bi-lstm siamese CapsuleNet 6:time series data درخواست پیاده سازی @RaminMousa

Machine Learning Refined — J. Watt, R. Borhani, A. K. Katsaggelos (en) 2016 #book #middle #theory @Machine_learn

Machine Learning Refined — J. Watt, R. Borhani, A. K. Katsaggelos (en) 2016 #book #middle #theory @Machine_learn
Machine Learning Refined — J. Watt, R. Borhani, A. K. Katsaggelos (en) 2016 #book #middle #theory @Machine_learn

Applied Text Analysis with Python — B. Bengfort, R. Bilbro, T. Ojeda (en) 2016 #book #middle #python @Machine_learn

Applied Text Analysis with Python — B. Bengfort, R. Bilbro, T. Ojeda (en) 2016 #book #middle #python @Machine_learn
Applied Text Analysis with Python — B. Bengfort, R. Bilbro, T. Ojeda (en) 2016 #book #middle #python @Machine_learn

Practical Machine Learning – Sunila Gollapudi (en) #book #middle #theory @Machine_learn

Practical Machine Learning – Sunila Gollapudi (en) #book #middle #theory @Machine_learn
Practical Machine Learning – Sunila Gollapudi (en) #book #middle #theory @Machine_learn

#Marcos Lopez de Prado-Advances in Financial Machine Learning-Wiley #2018 #book @Machine_learn

#Biostatistical modeling Frank Harrel #note @Machine_learn

#Biostatistical modeling Frank Harrel #note @Machine_learn
#Biostatistical modeling Frank Harrel #note @Machine_learn

🔴 OpenCV Computer Vision with Python بینایی ماشین با پایتون و opencv @Machine_learn

@CVision اخبار حوزه یادگیری عمیق و هوش مصنوعی مقالات و یافته های جدید یادگیری عمیق آموزشهای مرتبط با تنسرفلو و کراس بینایی ما
@CVision اخبار حوزه یادگیری عمیق و هوش مصنوعی مقالات و یافته های جدید یادگیری عمیق آموزشهای مرتبط با تنسرفلو و کراس بینایی ماشین و پردازش تصویر و ... #deep_learning #tensorflow #keras #computer_vision #vision @cvision