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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

Ko'proq ko'rsatish

📈 Telegram kanali Machine Learning analitikasi

Machine Learning (@machinelearning9) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 40 072 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 3 398-o'rinni va Suriya mintaqasida 232-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 40 072 obunachiga ega bo‘ldi.

23 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 379 ga, so‘nggi 24 soatda esa 30 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
  • Jalb etish (ER): Auditoriya o‘rtacha 1.92% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.16% ini tashkil etuvchi reaksiyalarni to‘playdi.
  • Post qamrovi: Har bir post o‘rtacha 770 marta ko‘riladi; birinchi sutkada odatda 466 ta ko‘rish yig‘iladi.
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📝 Tavsif va kontent siyosati

Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

Yuqori yangilanish chastotasi (oxirgi ma’lumot 24 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

40 072
Obunachilar
+3024 soatlar
+337 kunlar
+37930 kunlar
Postlar arxiv
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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