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Data Science & Machine Learning

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

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📈 Analytical overview of Telegram channel Data Science & Machine Learning

Channel Data Science & Machine Learning (@datasciencefun) in the English language segment is an active participant. Currently, the community unites 77 661 subscribers, ranking 2 015 in the Education category and 3 825 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 77 661 subscribers.

According to the latest data from 12 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -123 over the last 30 days and by -21 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 2.52%. Within the first 24 hours after publication, content typically collects 1.01% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 1 954 views. Within the first day, a publication typically gains 781 views.
  • Reactions and interaction: The audience actively supports content: the average number of reactions per post is 3.
  • Thematic interests: Content is focused on key topics such as learning, accuracy, distribution, panda, dataset.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“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”

Thanks to the high frequency of updates (latest data received on 13 November, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

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Posts Archive
Machine Learning Simplified: A Gentle Introduction to Supervised Learning PDF

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CS109a: Introduction to Data Science 👇👇 lectures
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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.

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.

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🚀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

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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 👍👍

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