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

Network:Machine Learning with Python India5 036 Education2 429...

📈 Analytical overview of Telegram channel Machine Learning with Python

Channel Machine Learning with Python (@codeprogrammer) in the English language segment is an active participant. Currently, the community unites 67 818 subscribers, ranking 2 429 in the Education category and 5 036 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 67 818 subscribers.

According to the latest data from 14 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 66 over the last 30 days and by 5 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 4.52%. Within the first 24 hours after publication, content typically collects 1.70% reactions from the total number of subscribers.
Post reach: On average, each post receives 3 064 views. Within the first day, a publication typically gains 1 155 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 5.
Thematic interests: Content is focused on key topics such as insidead, learning, degree, evaluation, algorithm.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

Thanks to the high frequency of updates (latest data received on 15 June, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

67 818

Subscribers

+524 hours

No data7 days

+6630 days

3 064

Post views

~ 1 15524 hours

~ 1 68748 hours

4.52%

Engagement rate

~ 4

Posts per day

Ads index

beta

Posts Archive

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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. ✅ To access these 150 top and unique Python libraries, you can use the following link:👇🏼 ┌ 🏷 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 🪄 https://t.me/codeprogrammer 🖼 😡More likes 😡 => more posts

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Repost from Data Science Books

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📚 NATURAL LANGUAGE PROCESSING (2023) 👁 Price: 5$ 🔄 Download it: https://www.patreon.com/DataScienceBooks/shop/natural-language-processing-textbook-64525 💬 Tags: #NLP

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🖥 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()

A word cloud is a visual representation of a list of categories/tags. The more often a word occurs, the larger the size it takes on in the cloud. pip install wordcloud 🥰 Github: https://github.com/amueller/word_cloud?ref=blog.electroica.com

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