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

Red:Free Courses with Certificate - Python Programming, Data Science, Java Coding, SQL, Web Development, AI, ML, ChatGPT Expert India4 234 Educación2 106...

📈 Análisis del canal de Telegram Data Science & Machine Learning

El canal Data Science & Machine Learning (@datasciencefun) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 75 833 suscriptores, ocupando la posición 2 106 en la categoría Educación y el puesto 4 234 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 75 833 suscriptores.

Según los últimos datos del 21 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 770, y en las últimas 24 horas de 8, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 3.15%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.09% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 2 385 visualizaciones. En el primer día suele acumular 827 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 3.
Intereses temáticos: El contenido se centra en temas clave como learning, accuracy, distribution, panda, dataset.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“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”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 22 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.

75 833

Suscriptores

+824 horas

+717 días

+77030 días

2 385

Visitas de la publicación

~ 82724 horas

~ 1 04148 horas

3.15%

Tasa de compromiso

~ 2

Mensajes por día

Ads index

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Archivo de publicaciones

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

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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