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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 67 818 suscriptores, ocupando la posición 2 429 en la categoría Educación y el puesto 5 036 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 67 818 suscriptores.
Según los últimos datos del 14 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 66, y en las últimas 24 horas de 5, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 4.52%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.70% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 3 064 visualizaciones. En el primer día suele acumular 1 155 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 5.
- 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 15 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.
67 818
Suscriptores
+524 horas
Sin datos7 días
+6630 días
Archivo de publicaciones
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6 of the best cloud notebooks for data science
⏺ Cloud notebooks are analytical tools that can be accessed only through an Internet browser without the need to install special software, and provide the possibility of running codes, analyzing data, and creating reports in an online environment.
🔃 In the following, I have provided you with 6 of the best cloud notebooks for data science projects , each of which has its own applications and capabilities in data analysis, programming, and data science project management.
┌ 🏷 6 Free Cloud Notebooks for DS
├ ✅ Deepnote
├ ✅ Kaggle
├ ✅ Hex
├ ✅ Colab
├ ✅ Naas
└ ✅ Datalore
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🟣 The largest data visualization tools with Python
🔥 The most powerful data visualization ecosystem
👨🏻💻 The PyViz ecosystem, with nearly 150 different libraries in 12 categories , is one of the most powerful tools to facilitate learning and using data visualization in Python. This ecosystem includes from the main visualizations to the graphic and location libraries and the creation of the dashboard.
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┌ 🏷 Data visualization in Python
└ 🚀 PyViz
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🔈 list of top 50 data science cheat sheets
🔘 From the day I started summarizing data science topics on LinkedIn, I decided to summarize each topic in a few pages. I finally came up with a list of 50 cheat sheets from various areas of data science. This list covers pretty much everything a data person might need, from how to plot with Matplotlib to using ChatGPT.
⏺ Python: link
⏺ Pandas library: link
⏺ NumPy library: link
⏺ Matplotlib library: link
⏺ seaborn library: link
⏺ scikit-learn library: link
⏺ TensorFlow library: link
⏺ Keras library: link
⏺ PyTorch framework: link
⏺ SQL language: link
👀 GeoPandas project: link
👀 Git version control system: link
👀 AWS cloud platform: link
✅ Azure cloud platform: link
✅ Google Cloud Platform cloud computing: link
✅ Docker platform: link
✅ Kubernetes platform: link
✅ The Linux Command Line training: link
✅ Jupyter notebook: link
✅️ Data preparation: link
✅️ Data Visualization: Link
✅️ Statistical inference: link
✅️ possibility: link
✅️ Linear Algebra: Link
✅️ Differential calculation: link
✅ Time series: link
✅ Natural language processing: link
✅ Neural network: link
✅ Deep Learning: Link
✅ Machine learning: link
✅ Apache Spark Framework: Link
✅ Apache Hadoop framework: link
✅ Big O Notation tool: link
✅ Regular Expression training: link
✅ Unix / Linux Permissions training: link
✅ Python String Formatting tutorial: link
✅ Flask framework: link
✅ Django framework: link
✅ plotly library: link
✅ PostgreSQL database: link
✅ MySQL database: link
✅ MongoDB database: link
✅ TensorFlow Probability library: link
✅ Chatbot GPT-3: link
✅ Training GPT-3 API Reference: link
✅ SciPy library: link
✅ ChatGPT chatbot: link
✅ Training Colors in Data Viz: link
✅ Geospatial DS in Python training: link
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🖥 25 free data science courses, Gen AI, ML, ...
◀️ From reputable universities and institutions
⛓ In universities, I am always asked about free resources for learning data science, and I thought it would be better to share these resources with you here. I hope this collection will be of great help to those who want to become professionals in the field of data science! 💯
🔄 Data science courses
⏺ Python for Everybody course ➡️ link
⏺ Data analysis with Python course ➡️ link
⏺ Databases and SQL course ➡️ link
⏺ Intro to Inferential Statistics course ➡️ link
⏺ Machine Learning Zoomcamp course ➡️ link
🔄 Data engineering courses
⏺ Data Engineering course ➡️ link
⏺ Data Engineer Learning course ➡️ link
⏺ Database Engineer course ➡️ link
⏺ Big Data Specialization course ➡️ link
⏺ Data Engineering Zoomcamp course ➡️ link
🔵 Machine learning courses
⏺ Intro to ML course ➡️ link
⏺ ML for Everybody course ➡️ link
⏺ ML course in Python with Scikit-Learn ➡️ link
⏺ ML Crash Course ➡️ link
⏺ Course CS229: ML ➡️ link
🟡 MLOps courses
⏺ Python Essentials for MLOps course ➡️ link
⏺ MLOps for Beginners course ➡️ link
⏺ MLOps Specialization course ➡️ link
⏺ MLOps Specialization course ➡️ link
⏺ Made with ML course ➡️ link
🔄 Productive artificial intelligence courses
⏺ Generative AI for Beginners course ➡️ link
⏺ Generative AI Fundamentals course ➡️ link
⏺ Intro to Generative AI course ➡️ link
⏺ Generative AI course with LLMs ➡️ link
⏺ Generative AI for Everyone course ➡️ link
☄️ 6 data science YouTube courses for beginners
⭐️ If you are looking to enter the field of data science and are going to start learning data science topics, these 6 free YouTube courses are a unique opportunity!
1️⃣ Python course with freeCodeCamp
📝 4.5 hour video that covers everything you need to become a Python programmer.
┌ 🏷 Python with freeCodeCamp
└ ◼️ LINK
2️⃣ statistics course with StatQuest
📝 One of the main and prerequisite topics for learning data science is statistics, which this learning course has made easier than ever.
┌ 🏷 Statistics with StatQuest
└ ◼️ LINK
3️⃣ Mathematics course with 3Blue1Brown
✍️ Learning linear algebra, neural networks and central limit theorem for data science.
┌ 🏷 Mathematics with 3Blue1Brown
└ ◼️ LINK
4️⃣ Data cleaning course with DataCamp
📝 Importance and techniques of how to obtain cleansed data and face the challenges of data cleansing.
┌ 🏷 Data Cleaning with DataCamp
└ ◼️ LINK
5️⃣ Machine learning course with Krish Naik
📝 6-hour video that introduces different aspects of ML, from linear regression to clustering algorithms.
┌ 🏷 Machine Learning with Krish Naik
└ ◼️ LINK
6️⃣ Data visualization course with Simplilearn
📝 Getting to know how to visualize data using Matplotlib, Seaborn and Bokeh libraries.
┌ 🏷 Data Visualizations with Simplilearn
└ ◼️ LINK
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Repost from Data Science Books
Which of the following methods do you prefer in the purchasing or donation process?
If there is another platform, please let me know @hussein_sheikho
📚 NATURAL LANGUAGE PROCESSING (2023)
👁 Price: 5$
🔄 Download it: https://www.patreon.com/DataScienceBooks/shop/natural-language-processing-textbook-64525
💬 Tags: #NLP
🖥 A little word cloud generator in Python
Creating a word cloud based on the
'cl.txt' file
Particularly useful for NLP tasks or social media analysis
from wordcloud import WordCloud
import matplotlib.pyplot as plt
# Read text from a file
with open('cl.txt', 'r', encoding='utf-8') as file:
text = file.read()
# Generate word cloud
wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text)
# Display the generated word cloud using matplotlib
plt.figure(figsize=(10, 5))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show()pip install wordcloud
🥰 Github: https://github.com/amueller/word_cloud?ref=blog.electroica.comhttps://t.me/community_bot/join?startapp=id_192-r_MTU2NDYxOV8xNzk3
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