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 14 058 مشترک است و جایگاه 9 091 را در دسته فناوری و برنامه‌ها و رتبه 28 236 را در منطقه الهند دارد.
📊 شاخص‌های مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 14 058 مشترک جذب کرده است.
بر اساس آخرین داده‌ها در تاریخ 27 اکتبر, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 97 و در ۲۴ ساعت گذشته برابر -7 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 9.29% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 2.10% واکنش نسبت به کل مشترکان کسب می‌کند.
- دسترسی پست‌ها: هر پست به طور میانگین 1 306 بازدید دریافت می‌کند. در اولین روز معمولاً 295 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 28 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.
14 058
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آرشیو پست ها
14 059
Loading an Excel file in Python
Excel files are packed with headers, logos, merged cells, and multiple sheets but pandas handles it all.
With just a few extra parameters, you can skip junk rows, pick exact columns,e.t.c
# Import the pandas library
import pandas as pd
# Specify the path to your Excel file (.xlsx or .xls)
filename = "data.xlsx"
# Read the Excel file into a DataFrame
# Common options you'll use all the time:
df = pd.read_excel(
filename,
sheet_name=0, # 0 = first sheet
header=0, # Row (0-indexed) to use as column names
skiprows=4, # Skip first 4 rows
nrows=1000, # Load only first 1000 rows
)
# Check the first five rows
df.head()14 059
Loading a CSV file in Python
CSV stands for Comma-Separated Values the most common format for tabular data everywhere.
With pandas, turning a CSV into a powerful, queryable DataFrame takes just a few clear lines.
# Import the pandas library
import pandas as pd
# Specify the path to your CSV file
filename = "data.csv"
# Read the CSV file into a DataFrame
df = pd.read_csv(filename)
#Checking the first five rows
df.head()14 059
Hey everyone 👋
Tomorrow we are kicking off a new short & free series called:
📊 Data Importing Series 📊
We’ll go through all the real ways to pull data into Python:
→ CSV, Excel, JSON and more
→ Databases & SQL databases
→ APIs, Google Sheets, even PDFs & web scraping
Short lessons, ready-to-copy code, zero boring theory.
First part drops tomorrow.
Turn on notifications so you don’t miss it 🔔
Who’s excited? React with a 🔥 if you are.
14 059
Normalization vs Standardization: Why They’re Not the Same
People treat these two as interchangeable. they’re not.
👉 Normalization (Min-Max scaling):
Compresses values to 0–1.
Useful when magnitude matters (pixel values, distances).
👉 Standardization (Z-score):
Centers data around mean=0, std=1.
Useful when distribution shape matters (linear/logistic regression, PCA).
🔑 Key idea:
Normalization preserves relative proportions.
Standardization preserves statistical structure.
Pick the wrong one, and your model’s geometry becomes distorted.
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📚 Data Science Riddle - CNN Kernels
Which convolution increases channel depth but not spatial size?
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✅ Complete AI (Artificial Intelligence) Roadmap 🤖🚀
1️⃣ Basics of AI
🔹 What is AI?
🔹 Types: Narrow AI vs General AI
🔹 AI vs ML vs DL
🔹 Real-world applications
2️⃣ Python for AI
🔹 Python syntax & libraries
🔹 NumPy, Pandas for data handling
🔹 Matplotlib, Seaborn for visualization
3️⃣ Math Foundation
🔹 Linear Algebra: Vectors, Matrices
🔹 Probability & Statistics
🔹 Calculus basics
🔹 Optimization techniques
4️⃣ Machine Learning (ML)
🔹 Supervised vs Unsupervised
🔹 Regression, Classification, Clustering
🔹 Scikit-learn for ML
🔹 Model evaluation metrics
5️⃣ Deep Learning (DL)
🔹 Neural Networks basics
🔹 Activation functions, backpropagation
🔹 TensorFlow / PyTorch
🔹 CNNs, RNNs, LSTMs
6️⃣ NLP (Natural Language Processing)
🔹 Text cleaning & tokenization
🔹 Word embeddings (Word2Vec, GloVe)
🔹 Transformers & BERT
🔹 Chatbots & summarization
7️⃣ Computer Vision
🔹 Image processing basics
🔹 OpenCV for CV tasks
🔹 Object detection, image classification
🔹 CNN architectures (ResNet, YOLO)
8️⃣ Model Deployment
🔹 Streamlit / Flask APIs
🔹 Docker for containerization
🔹 Deploy on cloud: Render, Hugging Face, AWS
9️⃣ Tools & Ecosystem
🔹 Git & GitHub
🔹 Jupyter Notebooks
🔹 DVC, MLflow (for tracking models)
🔟 Build AI Projects
🔹 Chatbot, Face recognition
🔹 Spam classifier, Stock prediction
🔹 Language translator, Object detector
14 059
📚 Data Science Riddle
Your model's loss fluctuates but doesn't decrease overall. What's the most likely issue?
14 059
The Difference Between Model Accuracy and Business Accuracy
A model can be 95% accurate…
yet deliver 0% business value.
Why❔
Because data science metrics ≠ business metrics.
📌 Examples:
- A fraud model catches tiny fraud but misses large ones
- A churn model predicts already obvious churners
- A recommendation model boosts clicks but reduces revenue
Always align ML metrics with business KPIs.
Otherwise, your “great model” is just a great illusion.
14 059
📚 Data Science Riddle
Your estimate has high variance. Best fix?
14 059
Covers Spark for ML, graph processing (GraphFrames), and integration with Hadoop from Stanford University.
14 059
📚 Data Science Riddle
A feature has low importance but domain experts insist it matters. What do you do?
14 059
📚 Data Science Riddle
You need fast reads of small files. What storage options fits best?
14 059
🛠️ Running Code in Jupyter Notebooks
Jupyter Notebooks let you write & run code interactively.
Here’s a quick guide to make your workflow smoother:
▶️ Kernel & Code Cells
- Each notebook is tied to a single kernel (e.g. IPython).
- Code cells are where you write and execute code.
⌨️ Useful Shortcuts
- Shift + Enter → run current cell, move to next
- Alt + Enter → run current cell, insert new one below
- Ctrl + Enter → run current cell, stay in place
🔄 Kernel Management
- Interrupt the kernel if code hangs.
- Restart kernel to reset memory & variables.
🖥️ Output Handling
- Results & errors appear directly under the cell.
- Long-running code outputs appear as they’re generated.
- Large outputs can be scrolled or collapsed for clarity.
💡 Pro Tip:
Always “Restart & Run All” before sharing or saving a notebook.
This ensures reproducibility and clean results.
👉 Explore
14 059
📚 Data Science Riddle
You want to prevent inconsistent data across environments. What helps most?
14 059
The Big Data bible from Stanford: MapReduce, Spark, recommendation systems, PageRank, locality-sensitive hashing, Large scale machine learning and mining social networks/streams all explained clearly with real algorithms you can code today. 500 pages of pure gold.
14 059
📚 Data Science Riddle
You want to detect extreme values visually in one plot. Which one is best?
14 059
📚 Data Science Riddle
A query runs slowly due to large table scans. What's the most targeted fix?
