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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 75 833 subscribers, ranking 2 106 in the Education category and 4 234 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 75 833 subscribers.

According to the latest data from 21 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 770 over the last 30 days and by 8 over the last 24 hours, overall reach remains high.

  • Verification status: Not verified
  • Engagement rate (ER): The average audience engagement rate is 3.15%. Within the first 24 hours after publication, content typically collects 1.09% reactions from the total number of subscribers.
  • Post reach: On average, each post receives 2 385 views. Within the first day, a publication typically gains 827 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 22 June, 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.

75 833
Subscribers
+824 hours
+717 days
+77030 days
Posts Archive
Which of the following tool can be used for data visualization?
Anonymous voting

Python for data science 2022.pdf2.42 MB

Artificial Intelligence in Games PDF

Python For Data Science 2022 PDF

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Distributed Machine Learning with Python PDF

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Introduction to R Programming👉

New Data Scientists - When you learn, it's easy to get distracted by Machine Learning & Deep Learning terms like "XGBoost", "Neural Networks", "RNN", "LSTM" or Advanced Technologies like "Spark", "Julia", "Scala", "Go", etc. Don't get bogged down trying to learn every new term & technology you come across. Instead, focus on foundations. - data wrangling - visualizing - exploring - modeling - understanding the results. The best tools are often basic, Build yourself up. You'll advance much faster. Keep learning!

The Machine Learning Solutions Architect Handbook PDF

Data Engineering with Google Cloud Platform PDF

Machine Learning Mastery with Python: Understand Your Data, Create Accurate Models and Work Projects End-to-end PDF

Numpy Handbook.pdf3.75 KB

Probability Cheat Sheet 👇👇 Cheat Sheet

💥Deep Learning with Pytorch by Prof.Yann LeCun (CNN Founder) This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. GitHub Link: https://atcold.github.io/pytorch-Deep-Learning/ YouTube Playlist: https://www.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq

Top 8 Github Repos to Learn Data Science and Python 1. All algorithms implemented in Python By: The Algorithms Stars ⭐️: 135K Fork: 35.3K Repo: https://github.com/TheAlgorithms/Python 2. DataScienceResources By: jJonathan Bower Stars ⭐️: 3K Fork: 1.3K Repo: https://github.com/jonathan-bower/DataScienceResources 3. Playground and Cheatsheet for Learning Python By: Oleksii Trekhleb ( Also the Image) Stars ⭐️: 12.5K Fork: 2K Repo: https://github.com/trekhleb/learn-python 4. Learn Python 3 By: Jerry Pussinen Stars ⭐️: 4,8K Fork: 1,4K Repo: https://github.com/jerry-git/learn-python3 5. Awesome Data Science By: Fatih Aktürk, Hüseyin Mert & Osman Ungur, Recep Erol. Stars ⭐️: 18.4K Fork: 5K Repo: https://github.com/academic/awesome-datascience 6. data-scientist-roadmap By: MrMimic Stars ⭐️: 5K Fork: 1.5K Repo: https://github.com/MrMimic/data-scientist-roadmap 7. Data Science Best Resources By: Tirthajyoti Sarkar Stars ⭐️: 1.8K Fork: 717 Repo: https://github.com/tirthajyoti/Data-science-best-resources/blob/master/README.md 8. Ds-cheatsheets By: Favio André Vázquez Stars ⭐️: 10.4K Fork: 3.1K Repo: https://github.com/FavioVazquez/ds-cheatsheets

Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals PDF

🚀Join our first free lessons and explore the fields of tech! You will find the answers to all your questions at our webinars
🚀Join our first free lessons and explore the fields of tech! You will find the answers to all your questions at our webinars. Open the link https://crst.co/imKOy, make your choice and apply now while there are still seats available. See you there! ▶️ May 26  - Manual QA Course. Free first lesson! ▶️ May 26 - Best Tech Remote Careers 2022: Systems Engineer ▶️ June 2 - Systems Engineer. Free first lesson! Special offer for all participants! ️✅ Apply by the link https://crst.co/mwOgo

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Python for Data Science: The Ultimate Step-by-Step Guide to Learn Python In 7 Days & NLP, Data Science from with Python PDF

A LITTLE GUIDE TO HANDLING MISSING DATA Having any Feature missing more than 5-10% of its values? you should consider it to be missing data or feature with high absence rate👀 How can you handle these missing values, ensuring you dont loose important part of your data🤷‍♀️ Not a problem😌. Here are important facts you must know😉 ✍️Instances with missing values for all features should be eliminated ✍️Features with high absence rate should either be eliminated or filled with values ✍️Missing values can be replaced using Mean Imputation or Regression Imputation ✍️ Be careful with mean imputation for it may introduce bias as it evens out all instances ✍️Regression Imputation might overfit your model ✍️Mean and Regression Imputation can't be applied to Text features with missing values ✍️Text Features with missing values can be eliminated if not needed in data ✍️Important Text Features with Missing values can be replaced with a new class or category labelled as uncategorized