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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π 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 68 482 subscribers, ranking 2 406 in the Education category and 4 586 in the India region.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 68 482 subscribers.
According to the latest data from 04 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -75 over the last 30 days and by 7 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 3.38%. Within the first 24 hours after publication, content typically collects 1.57% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 314 views. Within the first day, a publication typically gains 1 073 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 9.
- Thematic interests: Content is focused on key topics such as insidead, learning, degree, evaluation, algorithm.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βLearn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikhoβ
Thanks to the high frequency of updates (latest data received on 05 November, 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.
68 482
Subscribers
+724 hours
-597 days
-7530 days
Posts Archive
6 of the best cloud notebooks for data science
βΊ 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.
π 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.
β π· 6 Free Cloud Notebooks for DS
β β
Deepnote
β β
Kaggle
β β
Hex
β β
Colab
β β
Naas
β β
Datalore
π 205+ free data science and ML courses
π from the Udacity platform
π¨π»βπ» Udacity platform It has a wide range of machine learning and data science courses, some of these courses are free and some are paid.
βοΈ In this PDF file, I collected all the free Udacity courses on machine learning, data science, etc. and put them inside the PDF with an active link. Just click on the link of each course. So easily!ππΌ
π +205 Udacity FREE Courses
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π£ The largest data visualization tools with Python
π₯ The most powerful data visualization ecosystem
π¨π»βπ» 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.
β
To access these 150 top and unique Python libraries, you can use the following link:ππΌ
β π· Data visualization in Python
β π PyViz
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π list of top 50 data science cheat sheets
π 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.
βΊ Python: link
βΊ Pandas library: link
βΊ NumPy library: link
βΊ Matplotlib library: link
βΊ seaborn library: link
βΊ scikit-learn library: link
βΊ TensorFlow library: link
βΊ Keras library: link
βΊ PyTorch framework: link
βΊ SQL language: link
π GeoPandas project: link
π Git version control system: link
π AWS cloud platform: link
β
Azure cloud platform: link
β
Google Cloud Platform cloud computing: link
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Docker platform: link
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Kubernetes platform: link
β
The Linux Command Line training: link
β
Jupyter notebook: link
β
οΈ Data preparation: link
β
οΈ Data Visualization: Link
β
οΈ Statistical inference: link
β
οΈ possibility: link
β
οΈ Linear Algebra: Link
β
οΈ Differential calculation: link
β
Time series: link
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Natural language processing: link
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Neural network: link
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Deep Learning: Link
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Machine learning: link
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Apache Spark Framework: Link
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Apache Hadoop framework: link
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Big O Notation tool: link
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Regular Expression training: link
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Unix / Linux Permissions training: link
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Python String Formatting tutorial: link
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Flask framework: link
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Django framework: link
β
plotly library: link
β
PostgreSQL database: link
β
MySQL database: link
β
MongoDB database: link
β
TensorFlow Probability library: link
β
Chatbot GPT-3: link
β
Training GPT-3 API Reference: link
β
SciPy library: link
β
ChatGPT chatbot: link
β
Training Colors in Data Viz: link
β
Geospatial DS in Python training: link
πͺ https://t.me/codeprogrammer πΌ
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π₯ 25 free data science courses, Gen AI, ML, ...
βοΈ From reputable universities and institutions
β 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! π―
π Data science courses
βΊ Python for Everybody course β‘οΈ link
βΊ Data analysis with Python course β‘οΈ link
βΊ Databases and SQL course β‘οΈ link
βΊ Intro to Inferential Statistics course β‘οΈ link
βΊ Machine Learning Zoomcamp course β‘οΈ link
π Data engineering courses
βΊ Data Engineering course β‘οΈ link
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βΊ ML course in Python with Scikit-Learn β‘οΈ link
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βΊ Course CS229: ML β‘οΈ link
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βΊ Python Essentials for MLOps course β‘οΈ link
βΊ MLOps for Beginners course β‘οΈ link
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βΊ Made with ML course β‘οΈ link
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βΊ Generative AI course with LLMs β‘οΈ link
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βοΈ 6 data science YouTube courses for beginners
βοΈ 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!
1οΈβ£ Python course with freeCodeCamp
π 4.5 hour video that covers everything you need to become a Python programmer.
β π· Python with freeCodeCamp
β βΌοΈ LINK
2οΈβ£ statistics course with StatQuest
π One of the main and prerequisite topics for learning data science is statistics, which this learning course has made easier than ever.
β π· Statistics with StatQuest
β βΌοΈ LINK
3οΈβ£ Mathematics course with 3Blue1Brown
βοΈ Learning linear algebra, neural networks and central limit theorem for data science.
β π· Mathematics with 3Blue1Brown
β βΌοΈ LINK
4οΈβ£ Data cleaning course with DataCamp
π Importance and techniques of how to obtain cleansed data and face the challenges of data cleansing.
β π· Data Cleaning with DataCamp
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5οΈβ£ Machine learning course with Krish Naik
π 6-hour video that introduces different aspects of ML, from linear regression to clustering algorithms.
β π· Machine Learning with Krish Naik
β βΌοΈ LINK
6οΈβ£ Data visualization course with Simplilearn
π Getting to know how to visualize data using Matplotlib, Seaborn and Bokeh libraries.
β π· Data Visualizations with Simplilearn
β βΌοΈ 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
π NATURAL LANGUAGE PROCESSING (2023)
π Price: 5$
π Download it: https://www.patreon.com/DataScienceBooks/shop/natural-language-processing-textbook-64525
π¬ Tags: #NLP
π₯ 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
π₯° 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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1 ton = 2.5$
Collection of Free Courses to Learn Data Science, Data Engineering, Machine Learning, MLOps, and LLMOps
Begin your data professional journey from the basics of statistics to building a production-grade AI application.
https://www.kdnuggets.com/collection-of-free-courses-to-learn-data-science-data-engineering-machine-learning-mlops-and-llmops
