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Machine Learning

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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📈 Аналітичний огляд Telegram-каналу Machine Learning

Канал Machine Learning (@machinelearning9) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 40 072 підписників, посідаючи 3 398 місце в категорії Технології та додатки та 232 місце у регіоні Сирія.

📊 Показники аудиторії та динаміка

З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 40 072 підписників.

За останніми даними від 23 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на 379, а за останні 24 години на 30, загальне охоплення залишається високим.

  • Статус верифікації: Не верифікований
  • Рівень залученості (ER): Середній показник залученості аудиторії становить 1.92%. Протягом перших 24 годин після публікації контент зазвичай збирає 1.16% реакцій від загальної кількості підписників.
  • Охоплення публікацій: В середньому кожен допис отримує 770 переглядів. Протягом першої доби публікація в середньому набирає 466 переглядів.
  • Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 3.
  • Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як distance, insidead, gpu, learning, degree.

📝 Опис та контентна політика

Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

Завдяки високій частоті оновлень (останні дані отримано 24 червня, 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 Технології та додатки.

40 072
Підписники
+3024 години
+337 днів
+37930 день
Архів дописів
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
A free MIT guide to key computer vision concepts 📘 Link: https://visionbook.mit.edu/ 🔗 #ComputerVision #MIT #AI #MachineLearning #Tech #DataScience ✨ 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

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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