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 270 Educación2 110...

📈 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 75 821 suscriptores, ocupando la posición 2 110 en la categoría Educación y el puesto 4 270 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 75 821 suscriptores.

Según los últimos datos del 19 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 855, y en las últimas 24 horas de 10, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 3.21%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.26% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 2 431 visualizaciones. En el primer día suele acumular 953 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 20 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.

75 821

Suscriptores

+1024 horas

+1447 días

+85530 días

2 431

Visitas de la publicación

~ 95324 horas

~ 1 20148 horas

3.21%

Tasa de compromiso

~ 2

Mensajes por día

Ads index

beta

Archivo de publicaciones

75 822

Python Programming Notes 📝

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Practical Guide to Scikit-Learn for Data Science.pdf9.22 KB

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Statistics 101.pdf7.57 MB

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alex-galea-the-applied-data-science-workshop-second.pdf12.10 MB

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🔰 Python for Machine Learning & Data Science Masterclass 👇👇 https://t.me/datasciencefree/2

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Data Science Bookcamp (2021).pdf42.41 MB

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A Hands-On Introduction to Data Science Chirag Shah, 2020

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Building IoT Visualizations using Grafana Rodrigo Juan Hernandez, 2022

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Statistical Mechanics of Neural Networks ( Haiping Huang ). Springer 2021

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Netflix ML Architecture

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Repost from Python Projects & Resources

Advanced Python: Practical Database Examples.zip253.86 MB

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100 Data Structure interview Question & Answers .pdf8.07 KB

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Finland is a country with the fascinating nature, clean ecology and high living standards. It is a great place to grow children! We invite you to learn more about this wonderful country and join us for a free webinar “Relocation to Finland for Tech Specialists” June, 6 at 19:00, India Standard Time Online We’ll talk about: 1. What kind of Tech Talents are in demand in Finland? 2. Salary ranges and taxes 3. Family living costs 4. And what is the most important - how to succeed in job searching and easily pass interviews 500+ Tech Talents from various countries have moved to Finland in 2022 with our support, so can you! Try your hand! Join the channel and turn on notifications to get the link to the upcoming webinar: https://t.me/nerdsbay

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Overview of Machine Learning

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deep-learning-for-computer-architects.pdf2.77 MB

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🖥 Free Courses on Large Language Models ▪ChatGPT Prompt Engineering for Developers ▪LangChain for LLM Application Development ▪Building Systems with the ChatGPT API ▪Google Cloud Generative AI Learning Path ▪Introduction to Large Language Models with Google Cloud ▪LLM University ▪Full Stack LLM Bootcamp

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Python Notes 👇 https://t.me/pythondevelopersindia/576

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1. Can you explain how the memory cell in an LSTM is implemented computationally? The memory cell in an LSTM is implemented as a forget gate, an input gate, and an output gate. The forget gate controls how much information from the previous cell state is forgotten. The input gate controls how much new information from the current input is allowed into the cell state. The output gate controls how much information from the cell state is allowed to pass out to the next cell state. 2. What is CTE in SQL? A CTE (Common Table Expression) is a one-time result set that only exists for the duration of the query. It allows us to refer to data within a single SELECT, INSERT, UPDATE, DELETE, CREATE VIEW, or MERGE statement's execution scope. It is temporary because its result cannot be stored anywhere and will be lost as soon as a query's execution is completed. 3. List the advantages NumPy Arrays have over Python lists? Python’s lists, even though hugely efficient containers capable of a number of functions, have several limitations when compared to NumPy arrays. It is not possible to perform vectorised operations which includes element-wise addition and multiplication. They also require that Python store the type information of every element since they support objects of different types. This means a type dispatching code must be executed each time an operation on an element is done. 4. What’s the F1 score? How would you use it? The F1 score is a measure of a model’s performance. It is a weighted average of the precision and recall of a model, with results tending to 1 being the best, and those tending to 0 being the worst. 5. Name an example where ensemble techniques might be useful? Ensemble techniques use a combination of learning algorithms to optimize better predictive performance. They typically reduce overfitting in models and make the model more robust (unlikely to be influenced by small changes in the training data). You could list some examples of ensemble methods (bagging, boosting, the “bucket of models” method) and demonstrate how they could increase predictive power.