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
نمایش بیشتر
شبکه:📈 تحلیل کانال تلگرام Data science/ML/AI
کانال Data science/ML/AI (@datascience_bds) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 14 057 مشترک است و جایگاه 9 086 را در دسته فناوری و برنامه‌ها و رتبه 28 213 را در منطقه الهند دارد.
📊 شاخص‌های مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 14 057 مشترک جذب کرده است.
بر اساس آخرین داده‌ها در تاریخ 26 اکتبر, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 100 و در ۲۴ ساعت گذشته برابر 3 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 9.04% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 2.08% واکنش نسبت به کل مشترکان کسب می‌کند.
- دسترسی پست‌ها: هر پست به طور میانگین 1 271 بازدید دریافت می‌کند. در اولین روز معمولاً 293 بازدید جمع‌آوری می‌شود.
- واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 3 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند panda, learning, row, api, ethic تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاه‌های شخصی توصیف می‌کند:
“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...”
به لطف به‌روزرسانی‌های پرتکرار (آخرین داده در تاریخ 27 اکتبر, 2026)، کانال همواره به‌روز و دارای دسترسی بالاست. تحلیل‌ها نشان می‌دهد مخاطبان به‌طور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامه‌ها تبدیل کرده‌اند.
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🔁 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.
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Data Science Interview Questions and Answers.pdf13.55 MB
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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.
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Repost from Programming Quiz Channel
Which ML concept refers to splitting data into training and testing subsets?
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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 😉
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🧠 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.
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⚡️📊 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
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🤯📈 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.
