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Machine Learning

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Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

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📈 Аналитический обзор Telegram-канала Machine Learning

Канал Machine Learning (@machinelearning9) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 40 100 подписчиков, занимая 3 398 место в категории Технологии и приложения и 232 место в регионе Сирия.

📊 Показатели аудитории и динамика

С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 40 100 подписчиков.

Согласно последним данным от 23 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 379, а за последние 24 часа — 30, при этом общий охват остаётся высоким.

  • Статус верификации: Не верифицирован
  • Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 1.92%. В первые 24 часа после публикации контент обычно набирает 1.16% реакций от общего числа подписчиков.
  • Охват публикаций: В среднем каждый пост получает 770 просмотров. В течение первых суток публикация набирает 466 просмотров.
  • Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 3.
  • Тематические интересы: Контент сосредоточен на ключевых темах, таких как distance, insidead, gpu, learning, degree.

📝 Описание и контентная политика

Автор описывает ресурс как площадку для выражения субъективного мнения:
Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

Благодаря высокой частоте обновлений (последние данные получены 24 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Технологии и приложения.

40 100
Подписчики
+3024 часа
+337 дней
+37930 день
Архив постов
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PANDAS — CHEAT SHEET 1. DATA LOADING
Method          | What it does       
----------------+--------------------
pd.read_csv()   | Reads CSV file     
pd.read_excel() | Reads Excel file   
pd.read_sql()   | Reads data from SQL
pd.read_json()  | Reads JSON file    
2. DATA ANALYSIS
Method        | What it does              
--------------+---------------------------
df.head()     | Shows first rows          
df.info()     | Table information         
df.describe() | Statistics by columns     
df.shape      | Table size (rows, columns)
df.columns    | List of column names      
3. DATA SELECTION
Method     | What it does                     
-----------+----------------------------------
df.loc[]   | Selection by row and column names
df.iloc[]  | Selection by indices             
df.query() | Filtering by condition           
4. DATA CLEANING
Method               | What it does                   
---------------------+--------------------------------
df.isnull()          | Check for missing values (NULL)
df.dropna()          | Remove rows with missing values
df.fillna()          | Fill missing values            
df.drop_duplicates() | Remove duplicates              
df.astype()          | Change data type               
5. ANALYTICS
Method            | What it does               
------------------+----------------------------
df.groupby()      | Data grouping              
df.agg()          | Aggregation in groups      
df.value_counts() | Count of unique values     
df.mean()         | Mean value                 
df.median()       | Median                     
df.corr()         | Correlation between columns
6. DATA MERGING
Method      | What it does        
------------+---------------------
pd.merge()  | SQL JOIN by column  
pd.join()   | JOIN by index       
pd.concat() | Glue tables together
⭐ TOP 10 METHODS read_csv() head() info() loc[] iloc[] query() groupby() merge() fillna() sort_values()

PANDAS — CHEAT SHEET 1. DATA LOADING Method          | What it does       ----------------+-------------------- pd.read_csv()   | Reads CSV file     pd.read_excel() | Reads Excel file   pd.read_sql()   | Reads data from SQL pd.read_json()  | Reads JSON file    2. DATA ANALYSIS Method        | What it does              --------------+--------------------------- df.head()     | Shows first rows          df.info()     | Table information         df.describe() | Statistics by columns     df.shape      | Table size (rows, columns) df.columns    | List of column names      3. DATA SELECTION Method     | What it does                     -----------+---------------------------------- df.loc[]   | Selection by row and column names df.iloc[]  | Selection by indices             df.query() | Filtering by condition           4. DATA CLEANING Method               | What it does                   ---------------------+-------------------------------- df.isnull()          | Check for missing values (NULL) df.dropna()          | Remove rows with missing values df.fillna()          | Fill missing values            df.drop_duplicates() | Remove duplicates              df.astype()          | Change data type               5. ANALYTICS Method            | What it does               ------------------+---------------------------- df.groupby()      | Data grouping              df.agg()          | Aggregation in groups      df.value_counts() | Count of unique values     df.mean()         | Mean value                 df.median()       | Median                     df.corr()         | Correlation between columns 6. DATA MERGING Method      | What it does        ------------+--------------------- pd.merge()  | SQL JOIN by column  pd.join()   | JOIN by index       pd.concat() | Glue tables together ⭐ TOP 10 METHODS read_csv() head() info() loc[] iloc[] query() groupby() merge() fillna() sort_values()

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My favorite way to work with multiple filters in pandas.Series — not a chain of .loc, but a single mask. 🐼 The chain looks neat, but breaks on real data and easily gives unexpected results:
s = pd.Series([10, 15, 20, 25, 30])
s.loc[s > 20].loc[s % 2 == 1]
The problem is that the second .loc again looks at the original s, not the already filtered result. The logic gets messy. 🤯 It's more reliable to gather everything into one expression:
s = pd.Series([10, 15, 20, 25, 30])

mask = (s > 20) & (s % 2 == 1)
result = s.loc[mask]
One mask, one point of truth. ✅ It's easier to debug. Fewer surprises when the code grows. 🚀 #Pandas #Python #DataScience #CodingTips #DataEngineering #Debugging ✨ Join Best TG Channels https://t.me/addlist/0f6vfFbEMdAwODBk ⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A 🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more. ✅ 13 courses live + 40+ coming soon 🎯 One access, lifetime updates 🔑 Use code: PRESALE-BOOK-WAVE-2GFG 👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO

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A free MIT guide to key computer vision concepts 📘 Link: https://visionbook.mit.edu/ 🔗 #ComputerVision #MIT #AI #MachineLea
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Learn AI for free directly from top companies. 🚀 1 - Anthropic: anthropic.skilljar.com 2 - Google: grow.google/ai 3 - Meta: ai.meta.com/resources/ 4 - NVIDIA: developer.nvidia.com/cuda 5 - Microsoft: learn.microsoft.com/en-us/training/ 6 - OpenAI: academy.openai.com 7 - IBM: skillsbuild.org 8 - AWS: skillbuilder.aws 9 - DeepLearning.AI: deeplearning.ai 10 - Hugging Face: huggingface.co/learn 💬 Comment "Learning" if you find this helpful. 🔄 Repost so others can take help. 🔖 Must bookmark for future reference. #AI #MachineLearning #Tech #FreeLearning #DataScience #AIForAll https://t.me/CodeProgrammer

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