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 India29 106 Technologies & Applications8 932...

📈 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 13 899 subscribers, ranking 8 932 in the Technologies & Applications category and 29 106 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 13 899 subscribers.

According to the latest data from 27 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 81 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 8.01%. Within the first 24 hours after publication, content typically collects 2.06% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 113 views. Within the first day, a publication typically gains 287 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 28 August, 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.

13 899

Subscribers

+124 hours

-97 days

+8130 days

1 113

Post views

~ 28724 hours

~ 41048 hours

8.01%

Engagement rate

~ 1

Posts per day

Ads index

beta

Posts Archive

13 900

Repost from Cool GitHub repositories

lerobot This is an end-to-end library for robot learning. It handles the entire pipeline from loading and processing robotics datasets to training policies and deploying them in simulation or on real hardware. Creator: huggingface Stars ⭐️: 19,000 Forked by: 3,000 Github Repo: https://github.com/huggingface/lerobot #robotics #AI ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @github_repositories_bds for more cool repositories. This channel belongs to @bigdataspecialist group

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Top Data Science Tools By Function

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📚 Data Science Riddle A business team wants interpretable insights, not just predictions. What's the best model to start with?

Anonymous voting

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Notes on SQL for data management and analysis, including queries and integration with R, from University of South Carolina.

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Top 6 Types of AI Models

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📚 Data Science Riddle Why might your SQL join explode the number of rows unexpectedly?

Anonymous voting

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Skills Needed To Become Data Analyst

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Repost from Programming, data science, ML - free courses by Big Data Specialist

This is our latest post from Instagram page, saved as PDF. If you want a very comprehensive breakdown on what's LLMs are and how they actually work, you might want to check it out. Here's our Instagram post: Explaining LLMs

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Regularization: The Art of Keeping Models Humble Overfitting is the “ego problem” of models. They memorize training data and forget how to generalize. Regularization is how we humble them. ➡️ L1 (Lasso): Shrinks some weights to zero → performs feature selection. ➡️ L2 (Ridge): Reduces all weights slightly → smooths learning. ➡️ Dropout: Randomly removes neurons during training → prevents co-dependence. It’s not about punishment but it’s about discipline. Regularization teaches models to focus on patterns, not exceptions. 💭 Remember: The best models don’t just fit data. They respect uncertainty.

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📚 Data Science Riddle You discover your regression model performs poorly on recent data. The relationships between variables have shifted. What's this called?

Anonymous voting

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List of AI Project Ideas 👨🏻‍💻 Beginner Projects 🔹 Sentiment Analyzer 🔹 Image Classifier 🔹 Spam Detection System 🔹 Face Detection 🔹 Chatbot (Rule-based) 🔹 Movie Recommendation System 🔹 Handwritten Digit Recognition 🔹 Speech-to-Text Converter 🔹 AI-Powered Calculator 🔹 AI Hangman Game Intermediate Projects 🔸 AI Virtual Assistant 🔸 Fake News Detector 🔸 Music Genre Classification 🔸 AI Resume Screener 🔸 Style Transfer App 🔸 Real-Time Object Detection 🔸 Chatbot with Memory 🔸 Autocorrect Tool 🔸 Face Recognition Attendance System 🔸 AI Sudoku Solver Advanced Projects 🔺 AI Stock Predictor 🔺 AI Writer (GPT-based) 🔺 AI-powered Resume Builder 🔺 Deepfake Generator 🔺 AI Lawyer Assistant 🔺 AI-Powered Medical Diagnosis 🔺 AI-based Game Bot 🔺 Custom Voice Cloning 🔺 Multi-modal AI App 🔺 AI Research Paper Summarizer

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🚨 When & How Jupyter Notebooks Fail (And What To Use Instead) Hey Data Folks! 👩‍💻👨‍💻 Let’s talk about Jupyter Notebooks — powerful for exploration, but risky in production. Here’s why: ❌ Problems with Notebooks: 1. Out-of-order execution → hidden bugs. 2. Code changes after execution → inconsistent results. 3. Data leakage → sensitive info in outputs. 4. Security risks → tokens/keys exposed. 5. Hard to apply engineering practices → no modular code, testing, CI/CD. 6. Collaboration pain → merge conflicts, JSON issues. 7. Reproducibility issues → missing dependencies, versions. ✅ When They’re Useful: - Quick data exploration & prototyping. - Knowledge sharing (clean, runnable from top to bottom). - Teaching / hands-on tutorials (with solution notebooks). 🔧 What to Use Instead: - For production code → .py files + IDEs. - For workflows → template repos & reproducible setups. - For deployment → MLOps tools, pipelines, automation. 💡 Key Takeaways: - Use notebooks for exploration & teaching. - Use structured code + pipelines for production & deployment. - Always document dependencies, keep notebooks clean, never commit secrets!

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📚 Data Science Riddle Your batch ETL job runs slower each week despite no code change. What's your first suspect?

Anonymous voting

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Pandas Cheatsheet For Data Analysis

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Hey everyone 👋 Some time ago, I asked if I should start a Data Science educational series and since 96% of you said yes, I began creating it. But many of you also asked for real, hands-on experience with projects, not just lessons. So I decided to shift gears. It’s now becoming a full practical coding course! 💻 My goal is to help you build skills that get you job-ready, not just teach theory. It’s taking a bit longer, but I promise it’ll be worth it. Thank you all for your support and patience ❤️ I’ll let you know as soon as we’re ready to start!

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📚 Data Science Riddle During EDA(Explanatory Data Analysis), what's the main reason we use box plots?

Anonymous voting

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Discusses Modeling ETL workflows for data warehousing, including data sources and transformations, from Drexel University.

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📚 Data Science Riddle Why is data validation before model training critical in production ML systems?

Anonymous voting

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AI Engineer Roadmap

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Repost from Programming, data science, ML - free courses by Big Data Specialist

Latest post from our Instagram page, saved as PDF ☝️ You can also find it here: https://www.instagram.com/p/DQJrbCaDBpy/