Data Science & Machine Learning
رفتن به کانال در Telegram
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 77 616 مشترک است و جایگاه 2 020 را در دسته آموزش و رتبه 3 811 را در منطقه الهند دارد.
📊 شاخص‌های مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 77 616 مشترک جذب کرده است.
بر اساس آخرین داده‌ها در تاریخ 14 نوامبر, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -147 و در ۲۴ ساعت گذشته برابر -20 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 2.41% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 0.97% واکنش نسبت به کل مشترکان کسب می‌کند.
- دسترسی پست‌ها: هر پست به طور میانگین 1 872 بازدید دریافت می‌کند. در اولین روز معمولاً 755 بازدید جمع‌آوری می‌شود.
- واکنش‌ها و تعامل: مخاطبان به‌طور فعال حمایت می‌کنند؛ میانگین واکنش به هر پست 2 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 15 نوامبر, 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.
77 616
مشترکین
-2024 ساعت
-947 روز
-14730 روز
آرشیو پست ها
Which function is used to randomly reorder a Series or rows in Dataframe?
Which of the following is correct code to generate 20 normally distributed random numbers?
import numpy as np
Which type of data is separated into bins for analysis
Just a correction here
"np.nan" instead of "na.nan"
Hope you all are aware that mostly we
import numpy as np
Which of the following is incorrect code to replace na values with 0 in data?
Majority is correct
Well done guys 👍👍
Scikit learn can be specially used to solve classification, regression, clustering and dimensionality reduction problems
Which Scipy package can be used to solve differential equations?
Which Scipy package can be used for standard continuous and discrete probability distributions?
Some helpful Data science projects for beginners
https://www.kaggle.com/c/house-prices-advanced-regression-techniques
https://www.kaggle.com/c/digit-recognizer
https://www.kaggle.com/c/titanic
BEST RESOURCES TO LEARN DATA SCIENCE AND MACHINE LEARNING FOR FREE
https://developers.google.com/machine-learning/crash-course
https://www.kaggle.com/learn/overview
https://forums.fast.ai/t/recommended-python-learning-resources/26888
https://www.fast.ai/
https://imp.i115008.net/JrBjZR
https://ern.li/OP/1qvkxbfaxqj
ENJOY LEARNING 👍👍
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Python for Data Science
https://www.udemy.com/course/2021-learn-python-for-data-science-a-z-ver-59/?couponCode=PYTHON10
[Free for limited time only]
Which of the following is not a function of scikit learn?
An Artificial Neuron Network (ANN), popularly known as Neural Network is a computational model based on the structure and functions of biological neural networks. It is like an artificial human nervous system for receiving, processing, and transmitting information in terms of Computer Science.
Basically, there are 3 different layers in a neural network :
Input Layer (All the inputs are fed in the model through this layer)
Hidden Layers (There can be more than one hidden layers which are used for processing the inputs received from the input layers)
Output Layer (The data after processing is made available at the output layer)
Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.
Free Resources for Hacking and Programming
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https://t.me/joinchat/ZYXe60w7YisyNWEx
Top Bayesian Algorithms and Methods:
- Naive Bayes.
- Averages one-dependence estimators.
- Bayesian belief networks.
- Gaussian naive Bayes.
- Multinomial naive Bayes.
- Bayesian networks.
Data science Bootcamp with 5 projects
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https://www.udemy.com/course/data-science-bootcamp-with-python/?couponCode=FREE_UDS
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Some helpful Data science projects for beginners
https://www.kaggle.com/c/house-prices-advanced-regression-techniques
https://www.kaggle.com/c/digit-recognizer
https://www.kaggle.com/c/titanic
Intermediate Level Data science Projects
Black Friday Data : https://www.kaggle.com/sdolezel/black-friday
Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones
Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset
Million Song Data : https://www.kaggle.com/c/msdchallenge
Census Income Data : https://www.kaggle.com/c/census-income/data
Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset
Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2
Text mining : https://www.kaggle.com/kanncaa1/applying-text-mining
