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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Сеть:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert Индия4 234 Образование2 106...

📈 Аналитический обзор Telegram-канала Data Science & Machine Learning

Канал Data Science & Machine Learning (@datasciencefun) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 75 831 подписчиков, занимая 2 106 место в категории Образование и 4 234 место в регионе Индия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 75 831 подписчиков.

Согласно последним данным от 21 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 770, а за последние 24 часа — 8, при этом общий охват остаётся высоким.

Статус верификации: Не верифицирован
Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 3.15%. В первые 24 часа после публикации контент обычно набирает 1.09% реакций от общего числа подписчиков.
Охват публикаций: В среднем каждый пост получает 2 385 просмотров. В течение первых суток публикация набирает 827 просмотров.
Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 3.
Тематические интересы: Контент сосредоточен на ключевых темах, таких как learning, accuracy, distribution, panda, dataset.

📝 Описание и контентная политика

Автор описывает ресурс как площадку для выражения субъективного мнения:
“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”

Благодаря высокой частоте обновлений (последние данные получены 22 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Образование.

75 831

Подписчики

+824 часа

+717 дней

+77030 день

2 385

Просмотры поста

~ 82724 часа

~ 1 04148 часов

3.15%

Коэффициент вовлеченности

~ 2

Постов в день

Ads index

beta

Архив постов

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Which of the following tool can be used for data visualization?

Anonymous voting

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Python for data science 2022.pdf2.42 MB

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Artificial Intelligence in Games PDF

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Python For Data Science 2022 PDF

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Fundamentals_of_Deep_Learning,_2nd_Edition_Fourth_Early_Release.pdf10.94 MB

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Deep Learning Masterpiece .pdf14.46 MB

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Distributed Machine Learning with Python PDF

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Introduction to R Programming👉

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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!

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The Machine Learning Solutions Architect Handbook PDF

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Data Engineering with Google Cloud Platform PDF

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

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Numpy Handbook.pdf3.75 KB

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Probability Cheat Sheet 👇👇 Cheat Sheet

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💥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

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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

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🚀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

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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