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 68 475 subscribers, ranking 2 403 in the Education category and 4 579 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 68 475 subscribers.
According to the latest data from 03 November, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -74 over the last 30 days and by 12 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 4.07%. Within the first 24 hours after publication, content typically collects 1.55% reactions from the total number of subscribers.
- Post reach: On average, each post receives 2 789 views. Within the first day, a publication typically gains 1 062 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 8.
- 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 04 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.
68 475
Subscribers
+1224 hours
-827 days
-7430 days
Posts Archive
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.
t.me/HusseinSheikho
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
#MachineLearning #DeepLearning #BigData #Datascience #ML #Pandas #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras #SQL #Statistics #LLMs #AIagents
http://t.me/codeprogrammer βοΈ
Best LLMs Courses
Link: https://www.mltut.com/best-large-language-models-courses/
#MachineLearning #DeepLearning #BigData #Datascience #ML #HealthTech #DataVisualization #ArtificialInteligence #SoftwareEngineering #GenAI #deeplearning #ChatGPT #OpenAI #python #AI #keras #SQL #Statistics #LLMs #AIagents
http://t.me/codeprogrammer βοΈ
Master #DataAnalytics for real-world problems. I have curated a list of 40 Data Analytics Projects (solved & explained) that will help you build analytics skills using #Python.
Includes projects like:
1. Rainfall Trends in India Analysis
2. Netflix Content Strategy Analysis
3. Creating a Mutual Fund Plan
4. Stock Market Portfolio Optimization
5. Metro Operations Optimization
6. Analyzing the Impact of Carbon Emissions
Find this list of projects here:
https://thecleverprogrammer.com/2024/11/01/data-analytics-projects-with-python/
π Tags: #DataScience #Python #ML #AI #LLM #BIGDATA #Courses #Pandas #deeplearning #Projects
http://t.me/codeprogrammer βοΈ
πHow to make $15,000 in a month in 2024?
Easy!!! Lisa is now the hippest trader who is showing crazy results in the market!
She was able to make over $15,000 in the last month! βοΈ
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She was able to make over $15,000 in the last month! βοΈ
Right now she has started a marathon on her channel and is running it absolutely free. π‘
To participate in the marathon, you will need to :
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2. Write in private messages : βMarathonβ and start participating!
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βΆοΈ The best project oriented videos
π» To learn data science
π¨π»βπ» The best way to learn data science is to combine theoretical learning with practical projects. After you have mastered the main concepts and tools, stop wasting your time and start learning project-based and doing real projects!
β
Here I put 17 real data science projects in video form:π
1οΈβ£ Making machine learning transparent with LINK β¬
οΈ AI
2οΈβ£ LINK practical guide β¬
οΈ PySpark
3οΈβ£ Construction of LINK model β¬
οΈ SRKGPT
4οΈβ£ Building a recovery system β¬
οΈ LINK
5οΈβ£ Strengthening small models β¬
οΈ (2) , (1)
6οΈβ£ Set Falon and Lama-2 β¬
οΈ (2) , (1)
7οΈβ£ Embedding data using LINK β¬
οΈ BERT
8οΈβ£ Compression of models β¬
οΈ LINK
9οΈβ£ Dynamic pricing in business β¬
οΈ LINK
1οΈβ£ Product embedding in big data β¬
οΈ LINK
1οΈβ£ Twitter recommender systems β¬
οΈ LINK
1οΈβ£ How the Twitter algorithm works β¬
οΈ LINK
1οΈβ£ Pinterest advertising systems β¬
οΈ LINK
1οΈβ£ Search for the nearest neighbor β¬
οΈ LINK
1οΈβ£ LINK controller β¬
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1οΈβ£ Training neural networks β¬
οΈ LINK
β«οΈ Data dimensionality reduction with LINK β¬
οΈ PCA
5 Free Courses for Mastering LLMs
Link: https://machinelearningmastery.com/5-free-courses-mastering-llms
https://t.me/CodeProgrammer β
