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 š https://rebrand.ly/bigdatachannels DMCA: @disclosure_bds Contact: @mldatascientist
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Network:š 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 13 903 subscribers, ranking 8 919 in the Technologies & Applications category and 29 117 in the India region.
š Audience metrics and dynamics
Since its creation on Š½ŠµŠ²ŃŠ“Š¾Š¼Š¾, the project has demonstrated rapid growth, gathering an audience of 13 903 subscribers.
According to the latest data from 26 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 95 over the last 30 days and by -8 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 8.25%. Within the first 24 hours after publication, content typically collects 2.05% reactions from the total number of subscribers.
- Post reach: On average, each post receives 1 146 views. Within the first day, a publication typically gains 285 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.
š Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
āData science and machine learning hub
Python, SQL, stats, ML, deep learning, projects, PDFs, roadmaps and AI resources.
For beginners, data scientists and ML engineers
š https://rebrand.ly/bigdatachannels
DMCA: @disclosure_bds
Contact: @mldatasci...ā
Thanks to the high frequency of updates (latest data received on 27 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.
13 903
Subscribers
-824 hours
-57 days
+9530 days
Posts Archive
13 903
Our platform is finally ready. š
Do you remember the platform I told you we are building for you? š
Free learning materials, job offers, tech updates, Udemy couponsā¦ all in one place.
After almost 3 years of building, testing, talking to many of you and improving it step by stepā¦ itās finally in beta. āļø
A lot of you actually participated in developing this, as backend devs, frontend devs or designers. š§āš»That makes me insanely proud. This is truly built by us, for us. ā¤ļø Iām opening early access to a small group. If you want to be one of the first inside, test it, find bugs, suggest ideas, or just see whatās under the hoodā¦join the Beta Testers Group š https://t.me/+9vt9IKi6iGAxZDhk Letās make this thing amazing. Together. š
13 903
šø Your Model Workedā¦ Then the Bill Hit
You ship your model.
It runs fine.
Then the cloud bill lands and suddenly ML feels very real.
ā ļø Nobody warns you about this part.
š§ The Part Tutorials Skip
Training is a one-time cost.
Inference is forever.
Every request costs.
Every idle minute costs.
Every bad choice repeats on the bill.
Accuracy alone will not save you.
š„ Where Money Quietly Disappears
ā GPU when CPU was enough
ā Instances running with low traffic
ā No profiling, just vibes
ā Scaling for growth that is not there
It feels small until it is not.
š Quick Reality Checks
Before deploying, ask:
āWhat is my cost per request?
āDo users need this latency?
āCan the model be smaller?
āCan I batch requests?
If you are not measuring, you are just guessing.
š Real Talk
A slightly worse model that is way cheaper often wins.
Cool demos impress people.
Sustainable systems keep you building.
Learn this early and your future self will be very grateful.
13 903
ā
Computer Vision Basics You Should Know šļøāš§
Computer Vision (CV) enables machines to see, interpret, and understand images or videos like humans do.
1ļøā£ What is Computer Vision?
Itās a field of AI that trains computers to extract meaningful info from visual inputs (images/videos).
2ļøā£ Common Applications:
- Facial recognition (Face ID)
- Object detection (Self-driving cars)
- OCR (Reading text from images)
- Medical imaging (X-rays, MRIs)
- Surveillance & security
- Augmented Reality (AR)
3ļøā£ Key CV Tasks:
- Image classification: Whatās in the image?
- Object detection: Where is the object?
- Segmentation: What pixels belong to which object?
- Pose estimation: Detect body/face positions
- Image generation & enhancement
4ļøā£ Popular Libraries & Tools:
- OpenCV
- TensorFlow & Keras
- PyTorch
- Mediapipe
- YOLO (You Only Look Once)
- Detectron2
5ļøā£ Image Classification Example:
from tensorflow.keras.applications import MobileNetV2
model = MobileNetV2(weights="imagenet") 13 903
ā
Top Artificial Intelligence Concepts You Should Know š¤š§
š¹ 1. Natural Language Processing (NLP)
Use Case: Chatbots, language translation
ā Enables machines to understand and generate human language.
š¹ 2. Computer Vision
Use Case: Face recognition, self-driving cars
ā Allows machines to "see" and interpret visual data.
š¹ 3. Machine Learning (ML)
Use Case: Predictive analytics, spam filtering
ā AI learns patterns from data to make decisions without explicit programming.
š¹ 4. Deep Learning
Use Case: Voice assistants, image recognition
ā A type of ML using neural networks with many layers for complex tasks.
š¹ 5. Reinforcement Learning
Use Case: Game AI, robotics
ā AI learns by interacting with the environment and receiving feedback.
š¹ 6. Generative AI
Use Case: Text, image, and music generation
ā Models like ChatGPT or DALLĀ·E create human-like content.
š¹ 7. Expert Systems
Use Case: Medical diagnosis, legal advice
ā AI systems that mimic decision-making of human experts.
š¹ 8. Speech Recognition
Use Case: Voice search, virtual assistants
ā Converts spoken language into text.
š¹ 9. AI Ethics
Use Case: Bias detection, fair AI systems
ā Ensures responsible and transparent AI usage.
š¹ 10. Robotic Process Automation (RPA)
Use Case: Automating repetitive office tasks
ā Uses AI to handle rule-based digital tasks efficiently.
š” Learn these concepts to understand how AI is transforming industries!
š¬ Tap ā¤ļø for more!
13 903
š š¤ Run Your Own LOCAL LLM (Beginner Friendly)
LLMs are cool, but running your own local one hits different š
No cloud. No API keys. No limits.
š§© Step 1: Install Ollama
Install Ollama on your machine (works on Mac, Windows, Linux).
Once installed, open your terminal.
š Step 2: Run a model
ollama run llama3.2curl http://127.0.0.1:11434/api/generate \
-H "Content-Type: application/json" \
-d '{
"model": "llama3.2",
"prompt": "Explain overfitting like I am 12",
"stream": false
}'
13 903
ā
Deep Learning Basics You Should Know š§ ā”
Deep Learning is a subset of machine learning that uses neural networks with many layers to learn from data ā especially large, unstructured data like images, audio, and text.
1ļøā£ What is Deep Learning?
Itās an approach that mimics how the human brain works by using artificial neural networks (ANNs) to recognize patterns and make decisions.
2ļøā£ Common Applications:
- Image & speech recognition
- Natural Language Processing (NLP)
- Self-driving cars
- Chatbots & virtual assistants
- Language translation
- Healthcare diagnostics
3ļøā£ Key Components:
- Neurons: Basic units processing data
- Layers: Input, hidden, output
- Activation functions: ReLU, Sigmoid, Softmax
- Loss function: Measures prediction error
- Optimizer: Helps model learn (e.g. Adam, SGD)
4ļøā£ Neural Network Example:
from keras.models import Sequential
from keras.layers import Dense
model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(100,)))
model.add(Dense(1, activation='sigmoid')) 13 903
The Mechanism Behind Early Stopping
Training loss always drops.
Validation loss tells you when the model begins to memorize noise.
As training continues, the network fits smaller and smaller patterns in the training set.
Some of those patterns arenāt general.
The validation curve rises when the model crosses the point where learning becomes memorization.
Key takeawayā
Early stopping isnāt a āhackā. It is a direct detection of when your model starts overfitting.
13 903
+1
ā¹ļø Channel update
Based on your requests, we launched:
š§ Programming Quizzes
š Free Programming Books
The books channel was our most popular one before, but it was removed due to copyright issues.
Because of the huge interest, we decided to bring it back, sharing free and open books.
You also requested hands-on project based learning. We are working on it! šØāš»
Thanks for the support. More coming soon š
13 903
š²ā”ļø Gradient Boosting Variants: Still Here, Still Winning
Every year new ML models show upā¦
and yet gradient boosting keeps dominating š
Why it refuses to die šŖ
ā¢ Works insanely well on tabular data
ā¢ Needs little data compared to deep learning
ā¢ Strong performance with minimal tuning
ā¢ Interpretable enough for business use
The most popular variants today š„
ā¢ XGBoost fast and battle tested
ā¢ LightGBM extremely fast on large datasets
ā¢ CatBoost handles categorical features beautifully
Why they are still everywhere š
ā¢ Kaggle competitions
ā¢ Production ML systems
ā¢ Credit scoring, churn, pricing, fraud
Truth bomb š£
If your data is rows and columns,
gradient boosting is still your safest bet.
New models are cool.
Gradient boosting pays the bills š
