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 77 661 مشترک است و جایگاه 2 015 را در دسته آموزش و رتبه 3 825 را در منطقه الهند دارد.
📊 شاخص‌های مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 77 661 مشترک جذب کرده است.
بر اساس آخرین داده‌ها در تاریخ 12 نوامبر, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر -123 و در ۲۴ ساعت گذشته برابر -21 بوده و همچنان دسترسی گسترده‌ای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 2.52% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.01% واکنش نسبت به کل مشترکان کسب می‌کند.
- دسترسی پست‌ها: هر پست به طور میانگین 1 954 بازدید دریافت می‌کند. در اولین روز معمولاً 781 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 13 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.
77 661
مشترکین
-2124 ساعت
-767 روز
-12330 روز
آرشیو پست ها
Machine Learning Simplified: A Gentle Introduction to Supervised Learning PDF
Deep Learning A Visual Approach [2021] Andrew Glassner.pdf37.95 MB
Today's Interview QnAs
Company - United Breweries
Role- ML Engineer
1.What are the various steps involved in an data analytics project?
The steps involved in a data analytics project are:
Data collection
Data cleansing
Data pre-processing
EDA
Creation of train test and validation sets
Model creation
Hyperparameter tuning
Model deployment
2. Explain Star Schema.
Star schema is a data warehousing concept in which all schema is connected to a central schema.
3. What is root cause analysis?
Root cause analysis is the process of tracing back of occurrence of an event and the factors which lead to it. It’s generally done when a software malfunctions. In data science, root cause analysis helps businesses understand the semantics behind certain outcomes.
4. Define Confounding Variables.
A confounding variable is an external influence in an experiment. In simple words, these variables change the effect of a dependent and independent variable. A variable should satisfy below conditions to be a confounding variable :
Variables should be correlated to the independent variable.
Variables should be informally related to the dependent variable.
For example, if you are studying whether a lack of exercise has an effect on weight gain, then the lack of exercise is an independent variable and weight gain is a dependent variable. A confounder variable can be any other factor that has an effect on weight gain. Amount of food consumed, weather conditions etc. can be a confounding variable.
Three different learning styles in machine learning algorithms:
1. Supervised Learning
Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.
A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.
Example problems are classification and regression.
Example algorithms include: Logistic Regression and the Back Propagation Neural Network.
2. Unsupervised Learning
Input data is not labeled and does not have a known result.
A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.
Example problems are clustering, dimensionality reduction and association rule learning.
Example algorithms include: the Apriori algorithm and K-Means.
3. Semi-Supervised Learning
Input data is a mixture of labeled and unlabelled examples.
There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions.
Example problems are classification and regression.
Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.
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
Do you enjoy reading this channel?
Perhaps you have thought about placing ads on it?
To do this, follow three simple steps:
1) Sign up: https://telega.io/c/datasciencefun
2) Top up the balance in a convenient way
3) Create an advertising post
If the topic of your post fits our channel, we will publish it with pleasure.
Repost from Data Analytics
FREE Resources to learn Statistics
👇👇
Khan academy:
https://www.khanacademy.org/math/statistics-probability
Khan academy YouTube:
https://www.youtube.com/playlist?list=PL1328115D3D8A2566
Statistics by Marin :
https://www.youtube.com/playlist?list=PLqzoL9-eJTNBZDG8jaNuhap1C9q6VHyVa
Statquest YouTube channel:
https://www.youtube.com/user/joshstarmer
Free Statistics Books
http://www.sherrytowers.com/cowan_statistical_data_analysis.pdf
https://t.me/sqlspecialist/90
🚀Join us this week in the FREE Webinars and explore the fields of tech!
You will find the answers to all your questions at our webinars. Open the link https://crst.co/PSO8Z, make your choice and apply now while there are still seats available. See you there!
▶️ July 12, 11:00 AM PT - Tech Jobs for Beginners: Become a Software Tester
▶️ July 13, 11:00 AM PT - Best Remote Tech Jobs in 2022: Сareer Guidance for Everyone
▶️ July 13, 12:00 PM PT - Tech Sales Career Path to a Secure Your Future In 2022
▶️ July 14, 11:00 AM PT - Career Change: Get a Remote Job as a Software Tester
▶️ July 17 - Manual QA. First Free lesson
▶️ July 18 - Tech Sales Training. First Free lesson
▶️ July 19 - Tech Sales Program. First Free lesson
Special offer for all participants!
️✅ Apply by the https://crst.co/PSO8Z
While certificates have its own place to prove your skills, completing a course just for the sake of certificate is not going to help you at all. So whatever courses you take up, please make sure that you learn, practice and acquire that skill.
Python for Data Analysis & Visualization 2022
👇👇
https://bit.ly/3P5XsgO
Free Online Tech Courses from UDACITY
👇👇
Introduction to Python Programming
https://clnk.in/qFtT
Developing Android Apps with Kotlin
https://clnk.in/rUEo
Intro to Data Structures and Algorithms
https://clnk.in/rUEp
Introduction to Machine Learning Course
https://clnk.in/rUEq
Intro to TensorFlow for Deep Learning
https://clnk.in/rUEs
Version Control with Git
https://clnk.in/rUEv
Kotlin Bootcamp for Programmers
https://clnk.in/rUEw
Intro to Deep Learning with PyTorch
https://clnk.in/rUEy
ENJOY LEARNING 👍👍
The_MathWorks,_Inc_MATLAB_Deep_Learning_HDL_Toolbox_UG_The_MathWorks.pdf7.73 MB
