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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 2 417 en la categoría Educación y el puesto 5 033 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 11 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 56, y en las últimas 24 horas de 0, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 3.96%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 2.43% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 683 visualizaciones. En el primer día suele acumular 1 650 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 12 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
Sin datos24 horas
-127 días
+5630 días
Archivo de publicaciones
Data Visualization Cheat sheets and Resources
Corpus of 32 DV cheat sheets, 32 DV charts and 7 recommended DV books
📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV
http://t.me/codeprogrammer ⭐️
Data Visualization Cheat sheets and Resources
Corpus of 32 DV cheat sheets, 32 DV charts and 7 recommended DV books
✅ A comprehensive playlist to step into and master the world of machine learning and data science!
1️⃣ Data Science Principles:
😉 Essential Mathematics for Machine Learning: Link
😉 Overview and commonly used terms: Link
😉 Current interview trends: Link
😉 Linear Regression Guide: Link
😉 Logistic Regression Playlist: Link
😉 Classification criteria: Link
😉 Simple Bayes Classifier: Link
😉 Types of variables: Link
😉 Dimension reduction: Link
😉 Entropy, mutual entropy, KL divergence: link
😉 Dynamic Pricing Overview: Link
2️⃣ Building recommender systems:
😉 Netflix Calibrated Recommendations: Link
😉 Netflix Integrated Recommendation Model: Link
😉 The Evolution of Recommender Systems: Link
😉 Embedding tutorial: Link
😉 Annoy library for approximate nearest neighbor: link
😉 Reducer product for ANN: Link
😉 Model-based account recommendations: Link
😉 PID controller for diversity: link
😉 Instagram Recommender System: Link
😉 LinkedIn CTR Modeling: Link
😉 Meituan's two-tower recommendation model: Link
😉 Scalable Two Tower Model Question-Item: Link
😉 Twitter Recommender Algorithm: Link
😉 eBay language model for recommender system: link
😉 Overcoming biases for recommender systems: Link
3️⃣ Advanced Model Techniques and Applications:
😉 Importance of Model Calibration: Link
😉 Detect and monitor data changes: Link
😉 Neural Networks Training: Link
😉 Analytics-based advertising with Pinterest: Link
😉 Using Pre-trained Bert: Link
😉 Model Compression with Knowledge Distillation: Link
😉 Multi-Armed Bandit Strategies: Link
4️⃣ The world of large language models (LLMs):
😉 Conversational AI: Link
😉 The dual nature of conversational language models: link
😉 Frontier Developments in LLM: Link
😉 Improving the performance of open source LLMs: Link
😉 Building artificial intelligence in Shah Rukh Khan style: Link
📂 Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas
http://t.me/codeprogrammer ⭐️
Repost from Data Science Books
Pandas Cookbook (2025)
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✏️ Learn how to use Python for data analysis, modeling, and practical projects.
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✏️ Data analysis skills using Python libraries such as Pandas and NumPy.
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4️⃣ Data visualization course with Python
✏️ Learn to create advanced charts with tools like Matplotlib and Seaborn.
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✏️ Using the R language to analyze data and implement data science projects.
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6️⃣ Data visualization course with R
✏️ Learn how to create professional charts and visualize data with tools like ggplot2.
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7️⃣ Big Data Fundamentals Course
✏️ Learn the fundamentals of big data and related technologies such as Hadoop and Spark.
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8️⃣ Scala Programming Course for Data Science
✏️ Familiarity with the Scala language and its use in data analysis projects.
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9️⃣ Data Science for Business Course
✏️ Learn how to use data to improve business decisions.
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1️⃣ Deep Learning Fundamentals Course
✏️ Familiarity with the basics of deep learning and the concepts of neural networks.
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1️⃣ Deep Learning Course with TensorFlow
✏️ Working with TensorFlow to build and train deep learning models.
https://t.me/CodeProgrammer ✅
👩💻 Python Developer Roadmap is a guide for aspiring Python developers that helps structure and plan their learning and career development!
🌟 It provides a step-by-step plan that covers key aspects of Python development, from basic knowledge and syntax to more advanced topics such as databases, web development, testing, machine learning, and microservices development.
🔐 License: MIT
🖥 Github
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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
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
8️⃣ programming Languages
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Git commands basics
#MachineLearning #DeepLearning #BigData #Datascience #ML #Pandas #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras #SQL #Statistics #LLMs #AIagents
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Python Network Programming Cheat Sheet 🖥
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🐼 20 of the most used Pandas + PDF functions
👨🏻💻 The first time I used Pandas, I was supposed to quickly clean and organize a raw and complex dataset with the help of Pandas functions. Using the groupby function, I was able to categorize the data and get in-depth analysis of customer behavior. Best of all, it was when I used loc and iloc that I could easily filter the data.
✔️ Since then I decided to prepare a list of the most used Pandas functions that I use on a daily basis. Now this list is ready! In the following, I will introduce 20 of the best and most used Pandas functions:
🏳️🌈 read_csv(): Fast data upload from CSV files
🏳️🌈 head(): look at the first five rows of the database to start..
🏳️🌈 info(): Checking data structure such as data type and empty values.
🏳️🌈 describe(): Generate descriptive statistics for numeric columns.
🏳️🌈 loc[ ]: accesses rows and columns by label or condition.
🏳️🌈 iloc[ ]: Access data by row number.
🏳️🌈 merge(): Merge dataframes with common columns.
🏳️🌈 groupby(): Grouping for easier analysis.
🏳️🌈 pivot_table(): Summarize data in pivot table format.
🏳️🌈 to_csv(): Save data as a CSV file.
🏳️🌈 pd.concat(): Concatenate multiple dataframes in rows or columns.
🏳️🌈 pd.melt(): Convert wide format data to long format.
🏳️🌈 pd.pivot_table(): Create a pivot table with multiple levels.
🏳️🌈 pd.cut(): Split the data into specific intervals.
🏳️🌈 pd.qcut(): Sort data by percentage.
🏳️🌈 pd.merge(): Merge data in database style for advanced linking.
🏳️🌈 DataFrame.apply(): Apply a custom function to the data.
🏳️🌈 DataFrame.groupby(): Analyze grouped data.
🏳️🌈 DataFrame.drop_duplicates(): Drop duplicate rows.
🏳️🌈 DataFrame.to_excel(): Save data directly to Excel file.
┌ 🐼 Pandas Functions
└ 📄 PDF
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