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 693 Technologies & Applications9 381...

📈 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 13 667 subscribers, ranking 9 381 in the Technologies & Applications category and 31 693 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 13 667 subscribers.

According to the latest data from 08 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 150 over the last 30 days and by 4 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 7.97%. Within the first 24 hours after publication, content typically collects 2.27% reactions from the total number of subscribers.
Post reach: On average, each post receives 1 089 views. Within the first day, a publication typically gains 310 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 09 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.

13 667

Subscribers

+424 hours

+437 days

+15030 days

1 089

Post views

~ 31024 hours

~ 45848 hours

7.97%

Engagement rate

~ 1

Posts per day

Ads index

beta

Posts Archive

13 667

Data Structures in R

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An Artificial Neuron

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Layers of AI

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📚 Data Science Riddle What metric is commonly used to decide splits in decision trees?

Anonymous voting

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7 In Demand Data Analytics Skills

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Essential Pandas Methods For Data Science

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📚 Data Science Riddle In PCA, what do eigenvectors represent?

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AI Agents Quick Guide

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📚 Data Science Riddle Which algorithm groups data into clusters without labels?

Anonymous voting

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Extracting Features from Text - A Step-by-Step NLP Guide.pdf8.32 KB

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Dropout Explained Simply Neural networks are notorious for overfitting ( they memorize training data instead of generalizing). One of the simplest yet most powerful solutions? Dropout. During training, dropout randomly “drops” a percentage of neurons ( 20–50%). Those neurons temporarily go offline, meaning their activations aren’t passed forward and their weights aren’t updated in that round. 👉 What this does: ✔️ Forces the network to avoid relying on any single path. ✔️ Creates redundancy → multiple neurons learn useful features. ✔️ Makes the model more robust and less sensitive to noise. When testing happens, dropout is turned off, and all neurons fire but now they collectively represent stronger, generalized patterns. Imagine dropout like training with handicaps. It’s as if your brain had random “short blackouts” while studying, forcing you to truly understand instead of memorizing. And that’s why dropout remains a go-to regularization technique in deep learning and even in advanced architectures.

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Importance of Statistics and Exploratory Data Analysis

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What is RAG? 🤖📚 RAG stands for Retrieval-Augmented Generation. It’s a technique where an AI model first retrieves relevant info (like from documents or a database), and then generates an answer using that info. 🧠 Think of it like this: Instead of relying only on what it "knows", the model looks things up first - just like you would Google something before replying. 🔍 Retrieval + 📝 Generation = Smarter, up-to-date answers!

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Repost from Data visualization

How Data Science Roles are Changing With The Rise of AI

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📚 Data Science Riddle You have a dataset with 1,000 samples and 10,000 features. What’s a common problem you might face when training a model on this data?

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Morning brain teaser! 🧠 Let's see who's awake... 📚 Data Science Riddle You have a dataset with 1,000 samples and 10,000 features. What’s a common problem you might face when training a model on this data?

Anonymous voting

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Linear Algebra for Data Science.pdf6.12 KB

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🚀 Fast-Track Machine Learning Roadmap 2025 Mindset: Build first, learn just-in-time. Share progress publicly (GitHub + posts). Consistency > cramming. Weeks 1–2: Master Python, NumPy, Pandas, EDA, and data cleaning. Mini-win: load CSVs, handle missing data. Weeks 3–6: Learn ML fundamentals with scikit-learn — train/test splits, cross-validation, classifiers (LogReg, RF, XGB), and regressors. Project: spam classifier or house price predictor. Weeks 7–10: Dive into deep learning — tensors, autograd, PyTorch. Build CNN or text classifier + track experiments (Weights & Biases). Weeks 11–12: Specialize (NLP, CV, recommenders, MLOps) and ship a niche AI app. ———————— Weekly Routine: Mon-Tue: Learn concept + code example Wed-Thu: Build feature + log metrics Fri: Refactor + README + demo Sat: Share + get feedback + plan fixes Sun: Rest & review ———————— Portfolio Tips: Clear READMEs, reproducible env, demo videos, honest metric analysis. Avoid “math purgatory” and messy repos. Ship small every week! ———————— This approach gets you practical, portfolio-ready ML skills in ~3-4 months with real projects and solid evaluation for 2025 job markets!

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3 Types of Machine Learning