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
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📈 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 68 459 suscriptores, ocupando la posición 2 403 en la categoría Educación y el puesto 4 579 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 68 459 suscriptores.
Según los últimos datos del 02 noviembre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -42, y en las últimas 24 horas de -6, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 3.87%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.52% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 649 visualizaciones. En el primer día suele acumular 1 038 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 8.
- 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 03 noviembre, 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.
68 459
Suscriptores
-624 horas
-1037 días
-4230 días
Archivo de publicaciones
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
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✅ https://t.me/codeprogrammer
🗂 10 “Real Data Science Portfolio” Examples
📁 I've brought you 10 of the best portfolios from data science professionals, each of whom has followed a unique path! Check out these 10 and get inspired to build a strong portfolio of your own!👇1️⃣ Ken Jee Portfolio | Data Scientist ▶️ Field: Sports data analysis 👤 Link: Portfolio 2️⃣ Yassine Alouini's Portfolio | Kegel Master ▶️ Domain: Machine Learning and Kegel Competitions 👤 Link: Portfolio 3️⃣ Tatman Portfolio | Data Scientist ▶️ Domain: Natural Language Processing (NLP) 👤 Link: Portfolio 4️⃣ Robinson Portfolio | Data Scientist ▶️ Field: Statistical analysis and R programming 👤 Link: Portfolio 5️⃣ Siraj Raval's Portfolio | AI Instructor ▶️ Field: Machine Learning and Artificial Intelligence 👤 Link: Portfolio 6️⃣ Julia Silge's Portfolio | Data Scientist ▶️ Domain: Organized data and data visualization 👤 Link: Portfolio 7️⃣ Mueller Portfolio | Developer Scikit-Learn ▶️ Field: Machine learning and open source projects 👤 Link: Portfolio 8️⃣ Wickham Portfolio | Data Scientist ▶️ Area: R programming and data visualization 👤 Link: Portfolio 9️⃣ Portfolio of François Puget | Kegel Master ▶️ Domain: Advanced Machine Learning Techniques 👤 Link: Portfolio 🔟 Emily's Portfolio | Data Analyst at Disney ▶️ Domain: Data visualization and storytelling 👤 Link: Portfolio
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #SupervisedLearning #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming https://t.me/CodeProgrammer 🧠
Find your location on Map using Python
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #SupervisedLearning #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming https://t.me/CodeProgrammer 🖥
important courses in my twitter account
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🖥 190 Python projects
Link: https://amankharwal.medium.com/130-python-projects-with-source-code-61f498591bb
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #SupervisedLearning #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming https://t.me/CodeProgrammer 🖥
Repost from ML - DS/DA/DE - AI [Jobs, InterviewPrep] 🇮🇳
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More courses in our telegram channel
https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Only for 40 members
✅ Free ML Books You Should Know!
1⃣ Dive into Deep Learning: https://d2l.ai
🔢 Deep Learning: https://deeplearningbook.org
🔢 Interpretable Machine Learning: https://christophm.github.io/interpretable-ml-book/
🔢 Machine Learning Yearning: https://info.deeplearning.ai/machine-learning-yearning-book
🔢 Mathematics for Machine Learning: https://mml-book.github.io
🔢 An Introduction to Statistical Learning: https://statlearning.com
#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #SupervisedLearning #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming https://t.me/CodeProgrammer 🖥
Understanding Probability Distributions for Machine Learning with Python
In machine learning, probability distributions play a fundamental role for various reasons: modeling uncertainty of information and data, applying optimization processes with stochastic settings, and performing inference processes, to name a few. Therefore, understanding the role and uses of probability distributions in machine learning is essential for designing robust machine learning models, choosing the right algorithms, and interpreting outputs of a probabilistic nature, especially when building models with machine learning-friendly programming languages like Python.
This article unveils key probability distributions relevant to machine learning, explores their applications in different machine learning tasks, and provides practical Python implementations to help practitioners apply these concepts effectively. A basic knowledge of the most common probability distributions is recommended to make the most of this reading.
Read Free: https://machinelearningmastery.com/understanding-probability-distributions-machine-learning-python/
Generative AI for beginners by Microsoft
21 Lessons teaching everything you need to know to start building Generative AI applications
Enroll Free: https://github.com/microsoft/generative-ai-for-beginners
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The Hundred-Page Language Models Book
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https://github.com/aburkov/theLMbook
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Our channel on WhatsApp
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pandas for Data Science - Guide
In this learning path, you’ll get started with pandas and get to know the ins and outs of how you can use it to analyze data with Python.
pandas is a game-changer for #datascience and analytics, particularly if you came to #Python because you were searching for something more powerful than #Excel and #VBA. #pandas uses fast, flexible, and expressive data structures designed to make working with relational or labeled data both easy and intuitive.
Read: https://realpython.com/learning-paths/pandas-data-science/
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