Data science/ML/AI
Kanalga Telegram’da o‘tish
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
Ko'proq ko'rsatish
Tarmoq:📈 Telegram kanali Data science/ML/AI analitikasi
Data science/ML/AI (@datascience_bds) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 14 057 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 9 086-o'rinni va Hindiston mintaqasida 28 213-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 14 057 obunachiga ega bo‘ldi.
26 Oktabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 100 ga, so‘nggi 24 soatda esa 3 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 9.04% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 2.08% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 1 271 marta ko‘riladi; birinchi sutkada odatda 293 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 3 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent panda, learning, row, api, ethic kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“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...”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 27 Oktabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
14 057
Obunachilar
+324 soatlar
+87 kun
+10030 kun
Postlar arxiv
14 060
🔁 K-Fold Cross Validation
K-Fold exists to answer one honest question:
Will this model work on unseen data?A single train/test split is unreliable, especially with small datasets. So K-Fold simulates multiple “future tests” using the same data. 🧠 What It Really Does Instead of one split, we: 🔀 Divide data into K folds 🔁 Train the model K times 📦 Each time: one fold validates, the rest train 📊 Average the scores Every sample gets validated once, which reduces evaluation noise and gives a more trustworthy estimate. Important: It improves evaluation, not the model itself. ⚠️ What People Often Miss 🚫 Do NOT use K-Fold as your final test. Keep a separate test set ⚖️ Use Stratified K-Fold for imbalanced classification. ⏳ Do NOT use standard K-Fold for time series. 📊 K = 5 or 10 is usually enough. ✅ In short K-Fold is just: A smart way to reuse limited data to simulate multiple real-world tests. No magic. Just careful evaluation.
14 060
Data Science Interview Questions and Answers.pdf13.55 MB
14 060
VC Dimension
In theory courses, VC dimension appears abstract.
But it answers a deep question:
How complex is your model’s decision boundary?VC dimension measures the largest number of points a model can shatter (perfectly classify in all labelings). Why this is important❔ Two models with similar parameter counts can have very different capacities. For example: 📦 k-NN → very high effective capacity 📐 Linear classifier → limited capacity 🌳 Deep trees → extremely high capacity What you need to understand Generalization depends on capacity relative to data size. Too much capacity with little data leads to overfitting. ✅ VC dimension is about expressive power, not just number of parameters.
14 060
Repost from Programming Quiz Channel
Which ML concept refers to splitting data into training and testing subsets?
14 060
LLMs are getting insanely popular lately and suddenly everyone is talking about AI, chatbots, copilots, agents… so let’s clear it up 👇
So what are LLMs really? 🤔
LLMs = Large Language Models
Think of them as insanely smart text prediction machines that learned from tons of books, code, docs, and conversations 📚💻
Why everyone is obsessed right now 🔥
• They can write code 🧑💻
• Explain complex stuff like a friend 🗣
• Analyze data 📊
• Power chatbots, copilots, agents 🤖
• One model, MANY tasks
Why they exploded now 🚀
• GPUs got better and cheaper
• Open source models became really good
• Companies realized: this saves time and money 💰
The most famous LLMs you hear about 👀
• GPT-4 / GPT-4.1 by OpenAI
• Claude 3 by Anthropic
• Gemini by Google
• LLaMA 3 by Meta
• Mistral by Mistral AI
Where LLMs are actually used today 🛠
• Chatbots and AI assistants
• Writing SQL and Python
• Data analysis and reporting
• Customer support automation
• Internal company tools
Important truth 💡
LLMs are not magic 🪄
They are very powerful autocomplete with reasoning skills.
Learn how to use them properly and you are already ahead of most people 😉
14 060
🧠 LayerNorm vs BatchNorm: Same Goal, Different Behavior
Both techniques normalize activations, but they operate differently.
Batch Normalization
📦 Normalizes across the batch
⚡️ Depends on batch statistics
🖼 Works very well in CNNs
⚠️ Sensitive to small batch sizes
Layer Normalization
🔬 Normalizes across features per sample
📏 Independent of batch size
🤖 Preferred in transformers and NLP
✅ Stable for sequence models
Why transformers use LayerNorm❔
Sequence models often run with variable or small batches.
LayerNorm avoids reliance on batch statistics and stays stable.
✅ Rule of thumb
🖼 CNNs → BatchNorm
🤖 Transformers → LayerNorm
📌 They look similar mathematically but normalize along different axes.
14 060
⚡️📊 One Line Feature Scaling
Scaling features without touching sklearn 👀
df["age_scaled"] = (df["age"] - df["age"].mean()) / df["age"].std()14 060
Python for Data Analytics: The Ultimate Library Ecosystem (2026 Edition)
This wheel is the Python data stack that's recommended from raw scraping to production insights:
➡️ Data Manipulation → Pandas, Polars (the fast successor), NumPy
➡️ Visualization → Matplotlib, Seaborn, Plotly (interactive dashboards)
➡️ Analysis → SciPy, Statsmodels, Pingouin
➡️ Time Series → Darts, Kats, Tsfresh, sktime
➡️ NLP → NLTK, spaCy, TextBlob, transformers (BERT & friends)
➡️ Web Scraping → BeautifulSoup, Scrapy, Selenium
🔥 Pro tip from real projects:
👉Switch to Polars when Pandas starts choking on >1 GB datasets
👉 Use Plotly + Dash when stakeholders want interactive reports
👉 Combine Darts + Tsfresh for serious time-series feature engineering
14 060
🤯📈 Detect Outliers in 5 Lines
Simple Z score based outlier detection.
import numpy as np
z = (df["salary"] - df["salary"].mean()) / df["salary"].std()
outliers = df[np.abs(z) > 3]14 060
Pre-Chunking vs. Post-Chunking (On-Demand Chunking)
This visual breaks down two common ways to chunk documents in Retrieval-Augmented Generation (RAG) systems,and when each makes sense.
Pre-Chunking
Documents are cleaned, split into chunks, embedded, and stored ahead of time.
• Pros: Fast retrieval at query time, simpler runtime pipeline.
• Cons: Rigid,changing chunk size or strategy means reprocessing the entire dataset.
• Best for: Stable datasets, high-throughput apps, predictable queries.
Post-Chunking / On-Demand Chunking
Documents are stored whole; chunking happens after retrieval based on the user’s query.
• Pros: More flexible and query-aware, often more relevant context.
• Cons: Higher latency and infrastructure complexity.
• Best for: Evolving content, exploratory queries, precision-focused use cases.
🔑 Takeaway:
There’s no one-size-fits-all. If speed and scale matter most, pre-chunk. If adaptability and relevance are key, post-chunk. Many production systems even combine both.
