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Data science/ML/AI

Data science/ML/AI

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Data science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers πŸ‘‰ https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist

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Programming, data science, ML - free courses by Big Data SpecialistNetwork:Programming, data science, ML - free courses by Big Data SpecialistIndia31 607Technologies & Applications9 380

πŸ“ˆ Analytical overview of Telegram channel Data science/ML/AI

Channel Data science/ML/AI (@datascience_bds) in the English language segment is an active participant. Currently, the community unites 13 685 subscribers, ranking 9 380 in the Technologies & Applications category and 31 607 in the India 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 13 685 subscribers.

According to the latest data from 10 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 143 over the last 30 days and by 2 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 8.09%. Within the first 24 hours after publication, content typically collects 2.22% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 106 views. Within the first day, a publication typically gains 304 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 5.
  • Thematic interests: Content is focused on key topics such as panda, learning, row, api, ethic.

πŸ“ Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
β€œData science and machine learning hub Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources. For beginners, data scientists and ML engineers πŸ‘‰ https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatasci...”

Thanks to the high frequency of updates (latest data received on 11 June, 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 Technologies & Applications category.

13 685
Subscribers
+224 hours
+217 days
+14330 days
Posts Archive
Data science/ML/AI
Data science/ML/AI
13 685
CS109 Data Science By Harvard University βŒ›οΈ 12 weeks βœ… Video lectures βœ… Slides βœ… Lab exercises πŸ”— http://cs109.github.io/2015/pages/videos.html Note: i have issues with first video link but others are fine. #datascience #python #harvard βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 685
Classification of Deep Learning Models
Classification of Deep Learning Models

Data science/ML/AI
Data science/ML/AI
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Source codes for data science projects from my Instagram post: https://www.instagram.com/p/CJwDIpCA0nc/ 1. Build chatbots: https://dzone.com/articles/python-chatbot-project-build-your-first-python-pro 2. Credit card fraud detection: https://www.kaggle.com/renjithmadhavan/credit-card-fraud-detection-using-python 3. Fake news detection https://data-flair.training/blogs/advanced-python-project-detecting-fake-news/ 4.Driver Drowsiness Detection https://data-flair.training/blogs/python-project-driver-drowsiness-detection-system/ 5. Recommender Systems (Movie Recommendation) https://data-flair.training/blogs/data-science-r-movie-recommendation/ 6. Sentiment Analysis https://data-flair.training/blogs/data-science-r-sentiment-analysis-project/ 7. Gender Detection & Age Prediction https://www.pyimagesearch.com/2020/04/13/opencv-age-detection-with-deep-learning/ #data_science #projects βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 685
4 pillars of data science
4 pillars of data science

Data science/ML/AI
Data science/ML/AI
13 685
Repost from Programming, data science, ML - free courses by Big Data Specialist
Detailed Data Science Roadmap to become a Data Scientist

Data science/ML/AI
Data science/ML/AI
13 685
The Hierarchy of Data Jobs
The Hierarchy of Data Jobs

Data science/ML/AI
Data science/ML/AI
13 685
Repost from Programming, data science, ML - free courses by Big Data Specialist
Python course by kaggle Learn the most important language for data science. 🎬 8 lessons ⏰ 5 hours https://www.kaggle.com/learn/python #python βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @bigdataspecialist for more

Data science/ML/AI
Data science/ML/AI
13 685
Hey folks, some of you probably already know that, I have Instagram page where i share educational posts about data science and machine learning. Your support in form of follow and possibly engagement on my posts would be very appreciated. Instagram Page Link: http://Instagram.com/bigdataspecialist βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 685
Deep Learning
Deep Learning

Data science/ML/AI
Data science/ML/AI
13 685
πŸ‘©β€πŸ’» 5 FREE DATA SCIENCE COURSES FOR BEGINNERS πŸ‘©β€πŸ« CS109 Data Science (Harvard) - http://cs109.github.io/2015/pages/videos.html Data-Driven Decision Making (PwC) - https://www.coursera.org/learn/decision-making Machine Learning (Stanford) - https://www.coursera.org/learn/machine-learning Data Science Foundations (IBM) - https://cognitiveclass.ai/learn/data-science Data Science Specialization (JHU) - https://www.coursera.org/specializations/jhu-data-science Subscribe for more helpful data science learning materials and free courses #data_science βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 685
Data Scientist Resume Checklist
Data Scientist Resume Checklist

Data science/ML/AI
Data science/ML/AI
13 685
Best Statistic books for data science Practical statistics for data scientists by Peter Bruce and Andrew Bruce πŸ”— Book Link Think Stats by Allen B. Downey πŸ”— Book Link Computer Age Statistical Inference by Bradley Efron and Trevor Hastie πŸ”— Book Link Statistics in Plain English by Timothy C. Urdan πŸ”— Book Link #Statistics #books #data_science βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group

Data science/ML/AI
Data science/ML/AI
13 685
Anatomy of Data Scientistst
Anatomy of Data Scientistst

Data science/ML/AI
Data science/ML/AI
13 685
Data Scientist
Data Scientist

Data science/ML/AI
Data science/ML/AI
13 685
Repost from Programming, data science, ML - free courses by Big Data Specialist
The "Approaching (Almost) Any Machine Learning Problem" book. by 4x Kaggle grandmaster Abhishek Thakur

Data science/ML/AI
Data science/ML/AI
13 685
Hey, of course, If i find nice graphical representation I will send you, but now i can tell you how did I use every of these algorithms at my work. I used SVM for text and product classification (Some article belongs to sport category, some to business, medicine etc, similar with products, I used it to classify products into categories similar to what you have on Amazon. I used KNN for simple classification problems, but generally we don't use it much in production as there are more advanced ones. I used regression to predict continuous value as price of product. I used random forest (and Gradient boosting algorithms like LightGBM and XGBOOST) for predicting possibility that person will convert on some ad (for example that person will buy a product advertised in an ad). Both Random Forest and Gradient Boosting are based on decision trees, they are very similar but gradient boosting is more advanced. I used CNN for image recognition (finding patterns in images to recognize objects). I haven't used RNN (Recurrent neural networks ) much but they are used for problems that are recursive by their nature. For example good usage of it in my work would be for some NLP tasks (sentences could be considered as recursive so its used on text and speech data). Also they are used to simulate neuron activity in our brain). I used K-means for clusterization of articles or products into different unlabeled clusters. It helps to determine which articles/products are similar to each other. I used PCA (Principal Component Analysis) to reduce number of dimensions for datasets that have too many of them. It also helped me to remove personal data from some datasets and model them as doubles (instead of names, surnames, date of birth etc). I hope this helps. I will send this to main channel in case somebody else find it useful.

Data science/ML/AI
Data science/ML/AI
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photo content

Data science/ML/AI
Data science/ML/AI
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Data Distribution
Data Distribution

Data science/ML/AI
Data science/ML/AI
13 685
Repost from Programming, data science, ML - free courses by Big Data Specialist
The journey of Data Scientist
The journey of Data Scientist

Data science/ML/AI
Data science/ML/AI
13 685
⭐️ 15 Best Machine Learning Cheat Sheet ⭐️ 1- Supervised Learning https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf 2- Unsupervised Learning https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf 3- Deep Learning https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf 4- Machine Learning Tips and Tricks https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf 5- Probabilities and Statistics https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf 6- Comprehensive Stanford Master Cheat Sheet https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/super-cheatsheet-machine-learning.pdf 7- Linear Algebra and Calculus https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf 8- Data Science Cheat Sheet https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf 9- Keras Cheat Sheet https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf 10- Deep Learning with Keras Cheat Sheet https://github.com/rstudio/cheatsheets/raw/master/keras.pdf 11- Visual Guide to Neural Network Infrastructures http://www.asimovinstitute.org/wp-content/uploads/2016/09/neuralnetworks.png 12- Skicit-Learn Python Cheat Sheet https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf 13- Scikit-learn Cheat Sheet: Choosing the Right Estimator https://scikit-learn.org/stable/tutorial/machine_learning_map/ 14- Tensorflow Cheat Sheet https://github.com/kailashahirwar/cheatsheets-ai/blob/master/PDFs/Tensorflow.pdf 15- Machine Learning Test Cheat Sheet https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/ #machine_learning #deep_learning #scikit-learn #keras βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž–βž– Join @datascience_bds for more cool data science materials. *This channel belongs to @bigdataspecialist group