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
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Сітка:📈 Аналітичний огляд Telegram-каналу Data science/ML/AI
Канал Data science/ML/AI (@datascience_bds) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 14 064 підписників, посідаючи 9 052 місце в категорії Технології та додатки та 28 197 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 14 064 підписників.
За останніми даними від 25 жовтня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 97, а за останні 24 години на 4, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 8.86%. Протягом перших 24 годин після публікації контент зазвичай збирає 2.13% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 1 246 переглядів. Протягом першої доби публікація в середньому набирає 300 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 3.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як panda, learning, row, api, ethic.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“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...”
Завдяки високій частоті оновлень (останні дані отримано 26 жовтня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Технології та додатки.
14 064
Підписники
+424 години
+67 днів
+9730 днів
Архів дописів
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Repost from Coding Interview Preparation
Most Asked Topics in AI Engineer Interviews
Based on 2026 candidate reports
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🗃 SQL has a trick beginners often miss
Suppose you want:
The top 3 customers by total spending.You might write a complicated query. But first think in two steps: 1. Calculate spending per customer
GROUP BY customer_idORDER BY total_spending DESC
LIMIT 3SELECT
customer_id,
SUM(amount) AS total_spending
FROM orders
GROUP BY customer_id
ORDER BY total_spending DESC
LIMIT 3;
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Data Science & Machine Learning: What’s the Connection?
Data Science and Machine Learning are closely connected, but they are not the same thing.
Data Science is the broader field of using data to discover insights, solve problems, and support better decisions.
Machine Learning (ML) is one of the key technologies used within Data Science to make predictions and automate decisions from data.
🔹 Data Science : Collects, cleans, analyzes, and visualizes data
🔹 Machine Learning : Learns patterns from data and makes predictions
🔹 Together : Turn raw data into useful insights and intelligent solutions
For example, a company can use Data Science to analyze customer behavior and then use Machine Learning to predict which customers are likely to leave.
👉 In simple terms: Data Science works with data to understand what is happening, while Machine Learning helps computers learn from that data to predict what may happen next.
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🐼 Pandas Has a Built-In Way to Find Duplicates
Most people discover:
df.drop_duplicates()But before deleting anything, try:
df.duplicated().sum()This tells you how many duplicate rows exist. Want to see them?
df[df.duplicated()]Want to check duplicates based on specific columns?
df[df.duplicated(subset=["email"])]And here's a useful one:
df[df.duplicated(subset=["email"], keep=False)]keep=False marks every occurrence of the duplicate.
These commands come in handy when you're trying to understand why duplicates exist before removing them.
#Pandas
@datascience_bds
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🎯Recommendation Systems
Have you wver wondered why YouTube recommends certain videos, Spotify suggests songs you might like, or Netflix shows movies that match your interests? One major reason is Data Science.
Recommendation systems analyze user behavior and use that information to predict what a person is likely to enjoy or interact with.
🔍 How Does It Work?
Imagine you watch several videos about:
🤖 Artificial Intelligence
🐍 Python
📊 Data Science
The system collects signals such as:
• What you watch
• How long you watch it
• What you like or dislike
• What you search for
• What you skip
• What similar users watch
The system can then identify patterns and recommend content that matches your interests.
🧠 Common Approaches
1. Collaborative Filtering
"If users similar to you liked these items, you may like them too."
2. Content-Based Filtering
"You liked this type of content before, so here is more content with similar characteristics."
3. Hybrid Systems
Combine multiple approaches to produce better recommendations.
🚀 Where Are Recommendation Systems Used?
🎬 Netflix: Movies & shows
▶️ YouTube: Videos
🎵 Spotify: Music & playlists
🛒 Amazon : Products
📱 Social media: Posts and content
The important idea is simple:
Data → Patterns → Predictions → Recommendations
This is a real-world example of how Data Science turns massive amounts of user data into personalized experiences.
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🤖 50 Machine Learning Project Ideas
Looking to strengthen your Machine Learning portfolio? Here are 50 project ideas ranging from beginner to advanced.
🟢 Beginner
1. Iris Flower Classification
2. Titanic Survival Prediction
3. House Price Prediction
4. Student Score Prediction
5. Spam Email Detection
6. Movie Recommendation System
7. Customer Churn Prediction
8. Loan Approval Prediction
9. Wine Quality Prediction
10. Diabetes Prediction
11. Heart Disease Prediction
12. Car Price Prediction
13. Salary Prediction
14. Fake News Detection
15. Handwritten Digit Recognition
🟡 Intermediate
16. Sentiment Analysis on Reviews
17. Stock Price Prediction
18. Sales Forecasting
19. Credit Card Fraud Detection
20. Image Classification
21. Dog vs Cat Classifier
22. Traffic Sign Recognition
23. Face Mask Detection
24. Customer Segmentation
25. Music Recommendation System
26. Crop Recommendation System
27. Disease Prediction System
28. Energy Consumption Prediction
29. Resume Screening System
30. News Topic Classification
31. Emotion Detection from Text
32. Fake Job Posting Detection
33. Credit Risk Analysis
34. Movie Genre Classification
35. Flight Fare Prediction
🔴 Advanced
36. Object Detection with YOLO
37. Face Recognition Attendance System
38. AI Chatbot with NLP
39. Image Caption Generator
40. Speech Emotion Recognition
41. Plant Disease Detection
42. Brain Tumor Detection
43. Sign Language Recognition
44. AI Resume Analyzer
45. Medical Image Classification
46. Autonomous Lane Detection
47. Human Activity Recognition
48. DeepFake Detection
49. RAG-Based Question Answering System
50. AI Virtual Assistant
💡 Which Machine Learning project are you planning to build next? Let us know in the comments! 👇
@datascience_bds
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🔄 Why Cross Validation Is Better Than One Train/Test Split
Imagine flipping a coin 10 times. You might get 8 heads.
Does that mean the coin is biased?
Not necessarily.
A single train/test split can also give a misleading performance estimate.
Cross Validation repeats the process multiple times using different splits.
Instead of trusting one lucky result... You measure average performance across several experiments.
It's a much better estimate of how your model will perform on unseen data.
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🤖 RAG: How AI Can Answer Questions Using Your Own Data
Large Language Models are powerful, but they don't automatically know everything inside your private documents, databases, or company knowledge base.
That's where Retrieval-Augmented Generation (RAG) comes in.
🔹 1. User asks a question
The system receives the user's query.
🔹 2. Relevant information is retrieved
The query is converted into an embedding and compared against stored documents in a vector database.
🔹 3. Context is added
The most relevant information is provided to the language model as context.
🔹 4. The AI generates an answer
The model uses the retrieved information to produce a more relevant response.
A simple RAG pipeline looks like:
Documents → Chunking → Embeddings → Vector Database → Retrieval → LLM → Answer
💡 RAG doesn't retrain the model. It gives the model relevant information at the ti
#RAG
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📚 What I’m learning for 2027
I’ve been working in software and data science for over 8 years, but lately I’d be lying if I said I wasn’t a little worried about where our jobs are heading. 😅
The future feels more uncertain than ever, so I’ve been thinking seriously about what’s actually worth learning to stay relevant in 2027 and beyond.
I searched around for resources I’d personally want to invest my time in, and i figured why not sharing with you guys as well. This is my shortlist 👇
🧠 1. Let’s Build GPT from Scratch, Andrej Karpathy
Build a GPT yourself and finally understand what’s happening behind the API.
⏱️ ~2h
🔗 https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ
🔥 2. Neural Networks: Zero to Hero, Andrej Karpathy
A deeper dive into neural networks, backpropagation, language models, GPT and tokenization.
⏱️ ~19h
🔗 https://karpathy.ai/zero-to-hero.html
🤖 3. Hugging Face AI Agents Course
Learn how AI agents actually work: tools, actions, reasoning and agentic workflows.
💰 Free
🔗 https://huggingface.co/learn/agents-course/unit0/introduction
🏗 4. Designing Data-Intensive Applications, Martin Kleppmann
The classic for understanding databases, distributed systems, replication, partitioning, streams and designing systems that scale.
📖 ~600 pages
🔗 https://github.com/aasthas2022/SDE-Interview-and-Prep-Roadmap/blob/main/System%20Design/Resources/Designing%20Data%20Intensive%20Applications%20by%20Martin%20Kleppmann.pdf
⚙️ 5. Made With ML
The production side of ML: deployment, testing, monitoring, data pipelines and MLOps.
💰 Free
🔗 https://madewithml.com/#course
🎯 Why these?
My bet for 2027 is that writing code itself will become easier, while understanding AI + production systems + architecture will become even more valuable.
So that’s what I’m focusing on.
If you know a resource that belongs on this list please share it so everybody can find it valuable.
Hope this helps ❤️
