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Machine Learning with Python
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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho
Mostrar más📈 Análisis del canal de Telegram Machine Learning with Python
El canal Machine Learning with Python (@codeprogrammer) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 67 813 suscriptores, ocupando la posición 2 427 en la categoría Educación y el puesto 5 028 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 67 813 suscriptores.
Según los últimos datos del 13 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 60, y en las últimas 24 horas de -3, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 4.31%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.69% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 926 visualizaciones. En el primer día suele acumular 1 148 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 6.
- Intereses temáticos: El contenido se centra en temas clave como insidead, learning, degree, evaluation, algorithm.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 14 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.
67 813
Suscriptores
-324 horas
-77 días
+6030 días
Archivo de publicaciones
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https://www.kaggle.com/c/house-prices-advanced-regression-techniques
https://www.kaggle.com/c/digit-recognizer
https://www.kaggle.com/c/titanic
Intermediate Level Data science Projects
Black Friday Data : https://www.kaggle.com/sdolezel/black-friday
Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones
Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset
Million Song Data : https://www.kaggle.com/c/msdchallenge
Census Income Data : https://www.kaggle.com/c/census-income/data
Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset
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Repost from Machine Learning
Are you fluent in Python and want to evaluate your skills? 🤔
Do you want to learn Python? 🤗
Are you interested in learning through questions and answers? 😵
Do you want to receive the explanation of the question?💡
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✅ Learn PyTorch in 4 steps
👨🏻💻 With support for more than 200 different mathematical operations, PyTorch is one of the most powerful open source and computational libraries based on Python for machine learning. You don't need to look for different sources to learn this popular library, but the website of this library covers more than what you need.💯
2️⃣ Here, I will teach you how to learn PyTorch from scratch in 4 steps. 👌🏼
1️⃣ Text tutorial: If you don't know anything about Python, you should start learning from here.
⬅️ Topics: tensors, datasets and DataLoader, model building, optimization loop, saving, loading and using the model.
📄 PyTorch Basics
┘📎 Link: Learn the Basics
2️⃣ Video training: This section is very useful for those who prefer video content.
⬅️ Topics: introduction, tensor construction and model construction with pytorch.
📄 Introduction to PyTorch
┘📎 Link: Introduction to PyTorch
3️⃣ Examining examples: By viewing different examples, you will review what you have learned so far.
📄 Learning Python by example
┘📎 Link: Learning PyTorch with Examples
4️⃣ Coding: This step is vital! Your theoretical knowledge is worthless without coding! The Keras website is full of sample code that is great for getting started.
📄 Start coding
┘📎 Link: keras.io
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Repost from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
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Repost from Python Courses & Resources
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🔥 Large selection of interview questions on DS, AI, ML, DL, NLP, computer vision . Part 2
✅ Machine learning interview questions and answers for data scientists
✅ Deep Learning Interview Questions and Answers for Data Scientists
✅ Statistics questions
✅ Questions on probability theory
✅ Repository for a line of online courses on statistics
✅ Questions and answers for data scientists in Python
✅ SQL and DB Interview Questions and Answers for Data Scientists
Questions Based on Resumes
✅ Large Language Model (LLM) Interview Questions and Answers
✅ Questions and answers to interviews on computer vision part 1
✅ Computer vision interview questions and answers part 2
✅ Questions and answers to computer vision interviews part 3
