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Machine Learning with Python

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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📈 Аналітичний огляд Telegram-каналу Machine Learning with Python

Канал Machine Learning with Python (@codeprogrammer) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 68 475 підписників, посідаючи 2 403 місце в категорії Освіта та 4 579 місце у регіоні Індія.

📊 Показники аудиторії та динаміка

З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 68 475 підписників.

За останніми даними від 03 листопада, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на -74, а за останні 24 години на 12, загальне охоплення залишається високим.

  • Статус верифікації: Не верифікований
  • Рівень залученості (ER): Середній показник залученості аудиторії становить 4.07%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.55% реакцій від загальної кількості підписників.
  • Охоплення публікацій: В середньому кожен допис отримує 2 789 переглядів. Протягом першої доби публікація в середньому набирає 1 062 переглядів.
  • Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 8.
  • Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як insidead, learning, degree, evaluation, algorithm.

📝 Опис та контентна політика

Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

Завдяки високій частоті оновлень (останні дані отримано 04 листопада, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Освіта.

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This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visua
This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Python53

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🐼 20 of the most used Pandas + PDF functions 👨🏻‍💻 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. ✔️ 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: 🏳️‍🌈 read_csv(): Fast data upload from CSV files 🏳️‍🌈 head(): look at the first five rows of the database to start.. 🏳️‍🌈 info(): Checking data structure such as data type and empty values. 🏳️‍🌈 describe(): Generate descriptive statistics for numeric columns. 🏳️‍🌈 loc[ ]: accesses rows and columns by label or condition. 🏳️‍🌈 iloc[ ]: Access data by row number. 🏳️‍🌈 merge(): Merge dataframes with common columns. 🏳️‍🌈 groupby(): Grouping for easier analysis. 🏳️‍🌈 pivot_table(): Summarize data in pivot table format. 🏳️‍🌈 to_csv(): Save data as a CSV file. 🏳️‍🌈 pd.concat(): Concatenate multiple dataframes in rows or columns. 🏳️‍🌈 pd.melt(): Convert wide format data to long format. 🏳️‍🌈 pd.pivot_table(): Create a pivot table with multiple levels. 🏳️‍🌈 pd.cut(): Split the data into specific intervals. 🏳️‍🌈 pd.qcut(): Sort data by percentage. 🏳️‍🌈 pd.merge(): Merge data in database style for advanced linking. 🏳️‍🌈 DataFrame.apply(): Apply a custom function to the data. 🏳️‍🌈 DataFrame.groupby(): Analyze grouped data. 🏳️‍🌈 DataFrame.drop_duplicates(): Drop duplicate rows. 🏳️‍🌈 DataFrame.to_excel(): Save data directly to Excel file. ┌ 🐼 Pandas Functions └ 📄 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 ⭐️

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