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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
Show moreπ 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 67 813 subscribers, ranking 2 417 in the Education category and 5 033 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 67 813 subscribers.
According to the latest data from 11 June, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 56 over the last 30 days and by 0 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 3.96%. Within the first 24 hours after publication, content typically collects 2.43% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 683 views. Within the first day, a publication typically gains 1 650 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 6.
- 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 12 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.
67 813
Subscribers
No data24 hours
-127 days
+5630 days
Posts Archive
Data Visualization Cheat sheets and Resources
Corpus of 32 DV cheat sheets, 32 DV charts and 7 recommended DV books
π Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #DV
http://t.me/codeprogrammer βοΈ
Data Visualization Cheat sheets and Resources
Corpus of 32 DV cheat sheets, 32 DV charts and 7 recommended DV books
β
A comprehensive playlist to step into and master the world of machine learning and data science!
1οΈβ£ Data Science Principles:
π Essential Mathematics for Machine Learning: Link
π Overview and commonly used terms: Link
π Current interview trends: Link
π Linear Regression Guide: Link
π Logistic Regression Playlist: Link
π Classification criteria: Link
π Simple Bayes Classifier: Link
π Types of variables: Link
π Dimension reduction: Link
π Entropy, mutual entropy, KL divergence: link
π Dynamic Pricing Overview: Link
2οΈβ£ Building recommender systems:
π Netflix Calibrated Recommendations: Link
π Netflix Integrated Recommendation Model: Link
π The Evolution of Recommender Systems: Link
π Embedding tutorial: Link
π Annoy library for approximate nearest neighbor: link
π Reducer product for ANN: Link
π Model-based account recommendations: Link
π PID controller for diversity: link
π Instagram Recommender System: Link
π LinkedIn CTR Modeling: Link
π Meituan's two-tower recommendation model: Link
π Scalable Two Tower Model Question-Item: Link
π Twitter Recommender Algorithm: Link
π eBay language model for recommender system: link
π Overcoming biases for recommender systems: Link
3οΈβ£ Advanced Model Techniques and Applications:
π Importance of Model Calibration: Link
π Detect and monitor data changes: Link
π Neural Networks Training: Link
π Analytics-based advertising with Pinterest: Link
π Using Pre-trained Bert: Link
π Model Compression with Knowledge Distillation: Link
π Multi-Armed Bandit Strategies: Link
4οΈβ£ The world of large language models (LLMs):
π Conversational AI: Link
π The dual nature of conversational language models: link
π Frontier Developments in LLM: Link
π Improving the performance of open source LLMs: Link
π Building artificial intelligence in Shah Rukh Khan style: Link
π Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas
http://t.me/codeprogrammer βοΈ
Repost from Data Science Books
Pandas Cookbook (2025)
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https://t.me/CodeProgrammer β
π©βπ» Python Developer Roadmap is a guide for aspiring Python developers that helps structure and plan their learning and career development!
π 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.
π License: MIT
π₯ Github
https://t.me/CodeProgrammer β
Repost from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
0οΈβ£ Python
1οΈβ£ Data Science
2οΈβ£ Machine Learning
3οΈβ£ Data Visualization
4οΈβ£ Artificial Intelligence
5οΈβ£ Data Analysis
6οΈβ£ Statistics
7οΈβ£ Deep Learning
8οΈβ£ programming Languages
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Anyone who has programming channels with more than 2,000 subscribers and wants to generate income, contact me immediately.
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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
http://t.me/codeprogrammer β
Python Network Programming Cheat Sheet π₯
#MachineLearning #DeepLearning #BigData #Datascience #ML #Pandas #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras #SQL #Statistics #LLMs #AIagents
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Regression & Classification Loss Functions
#MachineLearning #DeepLearning #BigData #Datascience #ML #Pandas #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras #SQL #Statistics #LLMs #AIagents
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πΌ 20 of the most used Pandas + PDF functions
π¨π»βπ» 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.
βοΈ 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:
π³οΈβπ read_csv(): Fast data upload from CSV files
π³οΈβπ head(): look at the first five rows of the database to start..
π³οΈβπ info(): Checking data structure such as data type and empty values.
π³οΈβπ describe(): Generate descriptive statistics for numeric columns.
π³οΈβπ loc[ ]: accesses rows and columns by label or condition.
π³οΈβπ iloc[ ]: Access data by row number.
π³οΈβπ merge(): Merge dataframes with common columns.
π³οΈβπ groupby(): Grouping for easier analysis.
π³οΈβπ pivot_table(): Summarize data in pivot table format.
π³οΈβπ to_csv(): Save data as a CSV file.
π³οΈβπ pd.concat(): Concatenate multiple dataframes in rows or columns.
π³οΈβπ pd.melt(): Convert wide format data to long format.
π³οΈβπ pd.pivot_table(): Create a pivot table with multiple levels.
π³οΈβπ pd.cut(): Split the data into specific intervals.
π³οΈβπ pd.qcut(): Sort data by percentage.
π³οΈβπ pd.merge(): Merge data in database style for advanced linking.
π³οΈβπ DataFrame.apply(): Apply a custom function to the data.
π³οΈβπ DataFrame.groupby(): Analyze grouped data.
π³οΈβπ DataFrame.drop_duplicates(): Drop duplicate rows.
π³οΈβπ DataFrame.to_excel(): Save data directly to Excel file.
β πΌ Pandas Functions
β π PDF
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Best LLMs Courses
Link: https://www.mltut.com/best-large-language-models-courses/
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