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 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist

Network:Programming, data science, ML - free courses by Big Data Specialist India31 635 Technologies & Applications9 377...

📈 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 13 674 subscribers, ranking 9 377 in the Technologies & Applications category and 31 635 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 13 674 subscribers.

According to the latest data from 09 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 155 over the last 30 days and by 5 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 8.03%. Within the first 24 hours after publication, content typically collects 2.25% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 098 views. Within the first day, a publication typically gains 308 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.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“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 👉 https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatasci...”

Thanks to the high frequency of updates (latest data received on 10 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.

13 674

Subscribers

+524 hours

+197 days

+15530 days

1 098

Post views

~ 30824 hours

~ 45248 hours

8.03%

Engagement rate

~ 1

Posts per day

Ads index

beta

Posts Archive

13 674

3 Types of Machine Learning

13 674

R Cheatsheet - Part 3

13 674

Projects To Learn AI and LLM Engineering

13 674

PCA Dimensionality Reduction Cheatsheet

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R Cheatsheet - Part 2

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The Curse of Dimensionality 🧩 Here’s something that trips up many beginners: More features ≠ always better. When your dataset has too many features (dimensions), weird things happen: ⛔️ Distances between points become meaningless. ⛔️ Models struggle to generalize. ⛔️Training time explodes. 👉 Solution: techniques like PCA, feature selection, or just collecting smarter data instead of more data. Remember: Adding noise isn’t adding information.

13 674

R CHEATSHEET - Part 1

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Data Structure

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SQL for Data Science 📈.pdf2.25 KB

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Overfitting vs Underfitting 🎯 Why do ML models fail? Usually because of one of these two villains: Overfitting: The model memorizes training data but fails on new data. (Like a student who memorizes past exam questions but can’t handle a new one.) Underfitting: The model is too simple to capture patterns. (Like using a straight line to fit a curve.) The sweet spot? A model that generalizes well. Note: Regularization, cross-validation, and more data usually help fight these problems.

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AI vs ML vs Deep Learning 🤖 You’ve probably seen these 3 terms thrown around like they’re the same thing. They’re not. AI (Artificial Intelligence): the big umbrella. Anything that makes machines “smart.” Could be rules, could be learning. ML (Machine Learning): a subset of AI. Machines learn patterns from data instead of being explicitly programmed. Deep Learning: a subset of ML. Uses neural networks with many layers (deep) powering things like ChatGPT, image recognition, etc. Think of it this way: AI = Science ML = A chapter in the science Deep Learning = A paragraph in that chapter.

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Mathematical Foundations For Deep Learning

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Neural Networks and Deep Learning by Michael Nielsen.pdf5.82 MB

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🔥 Building models is fun… but here’s the real test: is your model actually any good, or just pretending? 👀 Topic: Evals in Data Science Evaluations—or evals—are our model’s report card. They tell us: - For a spam filter: Do we catch all spam (recall) without misclassifying grandma’s emails as junk (precision)? - For price prediction: How close are our predictions on average (RMSE)? But evals aren’t just about numbers—they influence trust, fairness, and real-world usefulness of our models. Discussion prompts: - What’s your go-to evaluation metric and why? - Seen a model that looked great on paper but flopped in reality? - Should fairness & usability be considered first-class evaluation metrics alongside accuracy? Free book to dive deeper: - Fairness and Machine Learning — rigorous, practical guide to evaluating models for fairness: https://fairmlbook.org/ Drop your thoughts below ⬇️

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TOP ML Interview Problems

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Machine_Learning_For_Dummies_by_John_Paul_Mueller,_Luca_Massaron.pdf11.81 MB

13 674