اکنون در دسترس! پژوهش تلگرام ۲۰۲۵ — مهمترین بینشهای سال 
Data Science & Machine Learning
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Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data
نمایش بیشتر📈 تحلیل کانال تلگرام Data Science & Machine Learning
کانال Data Science & Machine Learning (@datasciencefun) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 75 837 مشترک است و جایگاه 2 107 را در دسته آموزش و رتبه 4 219 را در منطقه الهند دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 75 837 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 22 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 728 و در ۲۴ ساعت گذشته برابر -2 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 3.00% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.05% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 2 278 بازدید دریافت میکند. در اولین روز معمولاً 794 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 learning, accuracy, distribution, panda, dataset تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
For collaborations: @love_data”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 23 ژوئن, 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 آموزش تبدیل کردهاند.
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آرشیو پست ها
👉A handy notebook on handling missing values
Link : 👇👇
https://www.kaggle.com/parulpandey/a-guide-to-handling-missing-values-in-python
A list of NLP Tutorials
Link : 👇👇
https://github.com/lyeoni/nlp-tutorial
“An Implementation and Explanation of the Random Forest in Python” by Will Koehrsen 👇👇
https://link.medium.com/GCWFv81v95
“How to analyse 100s of GBs of data on your laptop with Python” by Jovan Veljanoski 👇👇
https://link.medium.com/V8xS82Cax6
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Recall how many of the true positives were recalled (found), i.e. how many of the correct hits were also found. Its formula would be
Precision is one indicator of a machine learning model's performance – the quality of a positive prediction made by the model. Its formula would be?
Seeing Theory : A visual introduction to probability and statistics
Link :👇👇
https://seeing-theory.brown.edu/
“The Projects You Should Do to Get a Data Science Job” by Ken Jee
👇👇
https://link.medium.com/Q2DnxSGRO6
👉The Ultimate Guide to the Pandas Library for Data Science in Python
👇👇
https://www.freecodecamp.org/news/the-ultimate-guide-to-the-pandas-library-for-data-science-in-python/amp/
A Visual Intro to NumPy and Data Representation
.
Link : 👇👇
https://jalammar.github.io/visual-numpy/
Matplotlib Cheatsheet 👇👇
https://github.com/rougier/matplotlib-cheatsheet
SQL Cheatsheet 👇👇
https://websitesetup.org/sql-cheat-sheet/
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Start working on any project if you are a beginner and want to grow your career as a data scientist
You will learn much more as you practice and work on projects from yourself
You can find dataset in this channel or go to kaggle to find any random dataset and just work on it
Learning concepts is fine but most of the learnings come from projects
I know that might feel boring at first time but as you move forward, it become interesting
K-means vs DBScan ML Algorithm
DBScan is more robust to noise.
DBScan is better when the amount of clusters is difficult to guess.
K-means has a lower complexity, i.e. it will be much faster, especially with a larger amount of points.
What is the curse of dimensionality? Why do we care about it?
Data in only one dimension is relatively tightly packed. Adding a dimension stretches the points across that dimension, pushing them further apart. Additional dimensions spread the data even further making high dimensional data extremely sparse. We care about it, because it is difficult to use machine learning in sparse spaces.
Dimensionality reduction techniques
Singular Value Decomposition (SVD)
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
T-distributed Stochastic Neighbor Embedding (t-SNE)
Autoencoders
Fourier and Wavelet Transforms
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Chatbot project using ML
Before using this you have to install
Tensorflow, keras , pickle, nltk by using pip install in command prompt
Fake news Detection Machine Learning Project with 92%Accuracy
it contain compressed file in which "jupyter notebook file and dataset"✅
