Data science/ML/AI

Open in Telegram

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 300 Technologies & Applications8 986...

📈 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 13 905 subscribers, ranking 8 986 in the Technologies & Applications category and 29 300 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 13 905 subscribers.

According to the latest data from 25 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 109 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 7.77%. 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 1 080 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 4.
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 26 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.

13 905

Subscribers

+124 hours

+17 days

+10930 days

1 080

Post views

~ 28724 hours

~ 41648 hours

7.77%

Engagement rate

~ 1

Posts per day

Ads index

beta

Posts Archive

13 898

📈 Mean vs Median Suppose these are five salaries:

$35k, $38k, $42k, $44k, $2.5M

Mean (average): $531,800 Median (middle value): $42,000 The average suggests everyone is wealthy. The median tells a completely different story. 👉 Whenever your data contains extreme values (called outliers), the median often represents the data much better than the mean. That's why you'll often see median house prices and median income reported in the news.

13 898

SQL CHART

13 898

📉 Why We Split Data If you train and evaluate a model using the exact same dataset, you're only testing how well it remembers. That's why datasets are usually split into: 👉 Training set → The model learns from this. 👉 Validation set → Used to tune model settings. 👉 Test set → Used only once at the end to measure real performance. Think of it like studying for an exam. Reading the textbook is training. Practice questions are validation. The final exam is the test set.

13 898

🐼 Pandas: The Dangerous Difference Between loc and iloc Both select data. That's why beginners mix them up. The simplest way to remember is: loc → labels iloc → positions

df.loc[5]

means:

Give me the row whose label is 5.

On the other hand:

df.iloc[5]

means:

Give me the 6th row.

Those are not necessarily the same row. Especially after filtering. If your DataFrame index looks like:

then:

df.iloc[2]

returns the row at position 2. That's index label 4. This tiny distinction causes a surprising number of bugs.

13 898

📊 10 Websites Every Data Scientist Should Bookmark Whether you're learning data science or building production models, they'll save you a lot of time. Google Dataset Search Find millions of public datasets from universities, governments, and research organizations. Our World in Data High quality datasets with well-researched visualizations on health, climate, economics, energy, education, and more. UCI Machine Learning Repository One of the most widely used collections of datasets for machine learning practice and research. Papers with Code Research papers linked with official implementations, datasets, and benchmark leaderboards. OpenML A platform for sharing datasets, experiments, and reproducible machine learning workflows. Data.gov Over 300,000 public datasets published by the U.S. government. Awesome Public Datasets A massive GitHub repository of datasets organized by category. Google Colab Run Python notebooks in the cloud with free GPU access for many workloads. Hugging Face Datasets Thousands of ready-to-use datasets for NLP, computer vision, audio, and more. Kaggle Datasets Millions of datasets shared by the data science community. ⭐️ Save this post. You'll probably use these throughout your data science journey.

13 898

12 AI Frameworks Every AI Engineer Should Know

13 898

Repost from N/a

📘 R for Data Science ✍️ Authors: Garrett Grolemund, Hadley Wickham 🔗 Read Online #Datascience #R ──────────────────── 👉 @free_programming_books_bds 👈

13 898

✅ SQL Essentials for Data Science 🗄 👉 SQL remains an absolute must-have skill for anyone working in Data Science or Analytics. Virtually every organization manages its core information inside databases, and SQL is the key to extracting, transforming, and analyzing that data. 🔹 1. What is SQL? SQL = Structured Query Language 👉 Used to: ✔️ Query data ✔️ Filter records ✔️ Perform calculations ✔️ Uncover business insights 🔥 2. Popular Database Engines ✔️ PostgreSQL ✔️ MySQL ✔️ Snowflake ✔️ Google BigQuery 🔹 3. Basic SQL Query ✅ The SELECT Clause Used to fetch records from a table. SELECT * FROM customers; 👉 * retrieves every single column. 🔹 4. Fetch Specific Columns SELECT full_name, total_spent FROM customers; 🔹 5. WHERE Clause Used to apply filters to your data. SELECT * FROM customers WHERE age >= 25; 🔹 6. ORDER BY Sort your results. SELECT * FROM customers ORDER BY total_spent DESC; ✔️ ASC → Ascending (Lowest to Highest) ✔️ DESC → Descending (Highest to Lowest) 🔹 7. Aggregate Functions Used for summary statistics. Function: COUNT() Purpose: Counts the number of rows Function: SUM() Purpose: Adds values together Function: AVG() Purpose: Finds the mean value Function: MAX() Purpose: Finds the highest value Function: MIN() Purpose: Finds the lowest value ✅ Example SELECT AVG(total_spent) FROM customers; 🔹 8. GROUP BY Used to categorize data into buckets. SELECT country, SUM(total_spent) FROM customers GROUP BY country; 🔹 9. Why SQL is Critical? ✔️ #1 requested technical skill in job descriptions ✔️ Used daily by analysts, data engineers, & data scientists ✔️ Scales seamlessly with massive enterprise datasets

13 898

❌ Cross Entropy Isn't Measuring Accuracy Here's something that surprises a lot of people. These two predictions are both correct. Prediction A

Cat: 51%
Dog: 49%

Prediction B

Cat: 99.9%
Dog: 0.1%

Accuracy treats them exactly the same. Cross Entropy doesn't. It rewards confidence only when the model is correct. If the true class is "Cat": Prediction A gets a relatively high loss. Prediction B gets a very small loss. Now flip the prediction.

Cat: 0.1%
Dog: 99.9%

The loss explodes. That's because Cross Entropy isn't asking:

Did you get it right?

It's asking:

How confident were you in the correct answer?

That's why neural networks optimize Cross Entropy instead of accuracy. Accuracy is too coarse to guide learning.

13 898

ML Engineer vs AI Engineer

13 898

📍If Your Model Suddenly Gets Worse, Check These First Before retraining everything, inspect: • Data drift • Missing values • Feature distribution changes • New categories • Pipeline failures • Label quality Production issues are often data problems, not algorithm problems.

13 898

How Does Machine Learning Work?

13 898

15 GitHub Repositories For Machine Learning Engineers

13 898

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 @datascience_bds

13 898

We recently had a request from for Unsupervised Learning notes. To make this resource even more valuable for everyone, we decided to bundle them together with our Supervised Learning notes as well! Source: Princeton University Lecture Notes @datascience_bds

13 898

ETL Process For Data Analytics

13 898

✅ The Most Underrated Habit in Data Science 👉 Keep a modeling journal. After every experiment, write down: • What changed • Why you changed it • The metric before • The metric after • What you learned Six months later, this notebook becomes more valuable than your code.

13 898

The Little Book of Deep Learning.pdf4.52 MB

13 898

Power BI vs Microsoft Fabric

13 898

🚩7 Red Flags You Should Check in Every Dataset Before EDA, look for these. 🔻Duplicate rows 🔻Missing values that aren't random 🔻Impossible numbers (negative ages, future dates) 🔻Columns with only one value 🔻Categories with inconsistent spelling 🔻Target leakage 🔻Suspiciously perfect distributions Catching these early saves hours of debugging later.