Data Engineers
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๐Ÿฑ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐— ๐—œ๐—ง ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐—ฌ๐—ผ๐˜‚ ๐—–๐—ฎ๐—ป ๐—ง๐—ฎ๐—ธ๐—ฒ ๐—ข๐—ป๐—น๐—ถ๐—ป๐—ฒ ๐—ถ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ๐Ÿ˜

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๐— ๐—ฎ๐˜€๐˜๐—ฒ๐—ฟ ๐—ฃ๐—ฟ๐—ผ๐—บ๐—ฝ๐˜ ๐—˜๐—ป๐—ด๐—ถ๐—ป๐—ฒ๐—ฒ๐—ฟ๐—ถ๐—ป๐—ด ๐—ณ๐—ผ๐—ฟ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—ถ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ!๐Ÿ˜

Want to communicate with AI like a pro? ๐Ÿค–

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๐Ÿฑ ๐—™๐—ฅ๐—˜๐—˜ ๐— ๐—œ๐—ง ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐˜๐—ผ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป ๐—ง๐—ฒ๐—ฐ๐—ต, ๐—”๐—œ & ๐——๐—ฎ๐˜๐—ฎ ๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ๐Ÿ˜

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Tips to become a Data Engineer ๐Ÿ‘‡๐Ÿ‘‡

1. Data Engineering Basics: At its core, it's about efficiently moving and reshaping data from one place/format to another.
2. Be Curious: The field is vast. Dive deep, ask questions, and always be in the mode of learning and experimenting.
3. Master Data: Understand the intricacies of data types, where they originate, and how they're structured.
4. Programming: Grasping a language is crucial. If you're unsure, start with Python โ€“ it's versatile and widely used in the industry.
5. SQL: A timeless tool for querying databases. Mastering SQL will empower you to work with data across various platforms.
6. Command Line: Familiarizing yourself with command line operations can save a lot of time, especially for quick and repetitive tasks.
7. Know Computers: A basic understanding of how computers communicate and process information can guide better data engineering decisions.
8. Personal Projects: Practical experience is invaluable. Start projects, learn from them, and showcase your work on platforms like GitHub.
9. APIs and JSON: Many modern data sources are API-based. Understanding how to extract and manipulate JSON data will be a daily task.
10. Tools Mastery: Get proficient with your primary tools, but stay updated with emerging technologies and platforms.
11. Data Storage Basics: Know the difference and use-cases for Databases, Data Lakes, and Data Warehouses. Understand the distinction between OLTP (online transaction processing) and OLAP (online analytical processing).
12. Cloud Platforms: The cloud is the future. AWS, Azure, and GCP offer free tiers to start experimenting.
13. Business Acumen: A data engineer who understands business metrics and their implications can offer more value.
14. Data Grain: Dive deep into datasets to understand their finest level of detail. It aids in more precise querying and analytics.
15. Data Formats: Recognizing main data formats (like JSON, XML, CSV, SQLite, Database) will help you navigate different datasets with ease.
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Forwarded from Artificial Intelligence
๐Ÿฑ ๐—ฃ๐—ผ๐˜„๐—ฒ๐—ฟ๐—ณ๐˜‚๐—น ๐—š๐—ถ๐˜๐—›๐˜‚๐—ฏ ๐—ฅ๐—ฒ๐—ฝ๐—ผ๐˜€๐—ถ๐˜๐—ผ๐—ฟ๐—ถ๐—ฒ๐˜€ ๐˜๐—ผ ๐— ๐—ฎ๐˜€๐˜๐—ฒ๐—ฟ ๐—ฃ๐˜†๐˜๐—ต๐—ผ๐—ป ๐—ณ๐—ผ๐—ฟ ๐—™๐—ฟ๐—ฒ๐—ฒ๐Ÿ˜

Looking to Master Python for Free?โœจ๏ธ

These 5 GitHub repositories are all you need to level up โ€” from beginner to advanced! ๐Ÿ’ป

๐‹๐ข๐ง๐ค๐Ÿ‘‡:-

https://pdlink.in/3FG7DcW

๐Ÿ“Œ Save this post & share it with a Python learner!
โค2
Forwarded from Artificial Intelligence
๐Ÿฒ ๐—™๐—ฅ๐—˜๐—˜ ๐—ข๐—ป๐—น๐—ถ๐—ป๐—ฒ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป๐˜€ ๐—ง๐—ผ ๐—–๐—ต๐—ฎ๐—ป๐—ด๐—ฒ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—–๐—ฎ๐—ฟ๐—ฒ๐—ฒ๐—ฟ ๐—œ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ ๐Ÿ˜

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๐Ÿ’ฅ Start learning today and build the skills top companies want!โœ…๏ธ
โค1
ETL vs REVERSE ETL vs ELT
Forwarded from Artificial Intelligence
๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป ๐——๐—ฎ๐˜๐—ฎ ๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ ๐—ณ๐—ผ๐—ฟ ๐—™๐—ฅ๐—˜๐—˜ ๐˜„๐—ถ๐˜๐—ต ๐—›๐—ฎ๐—ฟ๐˜ƒ๐—ฎ๐—ฟ๐—ฑ ๐—จ๐—ป๐—ถ๐˜ƒ๐—ฒ๐—ฟ๐˜€๐—ถ๐˜๐˜†๐Ÿ˜

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Amazon Interview Process for Data Scientist position

๐Ÿ“Round 1- Phone Screen round
This was a preliminary round to check my capability, projects to coding, Stats, ML, etc.

After clearing this round the technical Interview rounds started. There were 5-6 rounds (Multiple rounds in one day).

๐Ÿ“ ๐—ฅ๐—ผ๐˜‚๐—ป๐—ฑ ๐Ÿฎ- ๐——๐—ฎ๐˜๐—ฎ ๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ ๐—•๐—ฟ๐—ฒ๐—ฎ๐—ฑ๐˜๐—ต:
In this round the interviewer tested my knowledge on different kinds of topics.

๐Ÿ“๐—ฅ๐—ผ๐˜‚๐—ป๐—ฑ ๐Ÿฏ- ๐——๐—ฒ๐—ฝ๐˜๐—ต ๐—ฅ๐—ผ๐˜‚๐—ป๐—ฑ:
In this round the interviewers grilled deeper into 1-2 topics. I was asked questions around:
Standard ML tech, Linear Equation, Techniques, etc.

๐Ÿ“๐—ฅ๐—ผ๐˜‚๐—ป๐—ฑ ๐Ÿฐ- ๐—–๐—ผ๐—ฑ๐—ถ๐—ป๐—ด ๐—ฅ๐—ผ๐˜‚๐—ป๐—ฑ-
This was a Python coding round, which I cleared successfully.

๐Ÿ“๐—ฅ๐—ผ๐˜‚๐—ป๐—ฑ ๐Ÿฑ- This was ๐—›๐—ถ๐—ฟ๐—ถ๐—ป๐—ด ๐— ๐—ฎ๐—ป๐—ฎ๐—ด๐—ฒ๐—ฟ where my fitment for the team got assessed.

๐Ÿ“๐—Ÿ๐—ฎ๐˜€๐˜ ๐—ฅ๐—ผ๐˜‚๐—ป๐—ฑ- ๐—•๐—ฎ๐—ฟ ๐—ฅ๐—ฎ๐—ถ๐˜€๐—ฒ๐—ฟ- Very important round, I was asked heavily around Leadership principles & Employee dignity questions.

So, here are my Tips if youโ€™re targeting any Data Science role:
-> Never make up stuff & donโ€™t lie in your Resume.
-> Projects thoroughly study.
-> Practice SQL, DSA, Coding problem on Leetcode/Hackerank.
-> Download data from Kaggle & build EDA (Data manipulation questions are asked)

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

ENJOY LEARNING ๐Ÿ‘๐Ÿ‘
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Forwarded from Artificial Intelligence
๐ˆ๐๐Œ ๐…๐‘๐„๐„ ๐‚๐ž๐ซ๐ญ๐ข๐Ÿ๐ข๐œ๐š๐ญ๐ข๐จ๐ง ๐‚๐จ๐ฎ๐ซ๐ฌ๐ž๐ฌ๐Ÿ˜

๐Ÿš€ Dive into the world of Data Analytics with these 6 free courses by IBM!

Gain practical knowledge and stand out in your career with tools designed for real-world applications.

All courses come with expert guidance and are free to access!๐ŸŽ‰

๐‹๐ข๐ง๐ค ๐Ÿ‘‡:- 
 
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Enroll For FREE & Get Certified ๐ŸŽ“
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Machine Learning Algorithms every data scientist should know:

๐Ÿ“Œ Supervised Learning:

๐Ÿ”น Regression
โˆŸ Linear Regression
โˆŸ Ridge & Lasso Regression
โˆŸ Polynomial Regression

๐Ÿ”น Classification
โˆŸ Logistic Regression
โˆŸ K-Nearest Neighbors (KNN)
โˆŸ Decision Tree
โˆŸ Random Forest
โˆŸ Support Vector Machine (SVM)
โˆŸ Naive Bayes
โˆŸ Gradient Boosting (XGBoost, LightGBM, CatBoost)


๐Ÿ“Œ Unsupervised Learning:

๐Ÿ”น Clustering
โˆŸ K-Means
โˆŸ Hierarchical Clustering
โˆŸ DBSCAN

๐Ÿ”น Dimensionality Reduction
โˆŸ PCA (Principal Component Analysis)
โˆŸ t-SNE
โˆŸ LDA (Linear Discriminant Analysis)


๐Ÿ“Œ Reinforcement Learning (Basics):
โˆŸ Q-Learning
โˆŸ Deep Q Network (DQN)


๐Ÿ“Œ Ensemble Techniques:
โˆŸ Bagging (Random Forest)
โˆŸ Boosting (XGBoost, AdaBoost, Gradient Boosting)
โˆŸ Stacking

Donโ€™t forget to learn model evaluation metrics: accuracy, precision, recall, F1-score, AUC-ROC, confusion matrix, etc.

Free Machine Learning Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

React โค๏ธ for more free resources
โค2
Forwarded from Artificial Intelligence
๐Ÿฐ ๐—›๐—ถ๐—ด๐—ต-๐—œ๐—บ๐—ฝ๐—ฎ๐—ฐ๐˜ ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป๐˜€ ๐˜๐—ผ ๐—Ÿ๐—ฎ๐˜‚๐—ป๐—ฐ๐—ต ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—–๐—ฎ๐—ฟ๐—ฒ๐—ฒ๐—ฟ ๐—ถ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ๐Ÿ˜

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๐Ÿญ๐Ÿฌ๐Ÿฌ๐Ÿฌ+ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฒ๐—ฑ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐—ฏ๐˜† ๐—œ๐—ป๐—ณ๐—ผ๐˜€๐˜†๐˜€ โ€“ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป, ๐—š๐—ฟ๐—ผ๐˜„, ๐—ฆ๐˜‚๐—ฐ๐—ฐ๐—ฒ๐—ฒ๐—ฑ!๐Ÿ˜

๐Ÿš€ Looking to upgrade your skills without spending a rupee?๐Ÿ’ฐ

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Planning for Data Science or Data Engineering Interview.

Focus on SQL & Python first. Here are some important questions which you should know.

๐ˆ๐ฆ๐ฉ๐จ๐ซ๐ญ๐š๐ง๐ญ ๐’๐๐‹ ๐ช๐ฎ๐ž๐ฌ๐ญ๐ข๐จ๐ง๐ฌ

1- Find out nth Order/Salary from the tables.
2- Find the no of output records in each join from given Table 1 & Table 2
3- YOY,MOM Growth related questions.
4- Find out Employee ,Manager Hierarchy (Self join related question) or
Employees who are earning more than managers.
5- RANK,DENSERANK related questions
6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.)
7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN.
8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers.
9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure.
10-Identify and remove duplicate records from a table.

๐ˆ๐ฆ๐ฉ๐จ๐ซ๐ญ๐š๐ง๐ญ ๐๐ฒ๐ญ๐ก๐จ๐ง ๐ช๐ฎ๐ž๐ฌ๐ญ๐ข๐จ๐ง๐ฌ

1- Reversing a String using an Extended Slicing techniques.
2- Count Vowels from Given words .
3- Find the highest occurrences of each word from string and sort them in order.
4- Remove Duplicates from List.
5-Sort a List without using Sort keyword.
6-Find the pair of numbers in this list whose sum is n no.
7-Find the max and min no in the list without using inbuilt functions.
8-Calculate the Intersection of Two Lists without using Built-in Functions
9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response.
10-Implement a function to fetch data from a database table, perform data manipulation, and update the database.

Join for more: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

ENJOY LEARNING ๐Ÿ‘๐Ÿ‘
โค2
๐—™๐—ฟ๐—ฒ๐—ฒ ๐—ฃ๐˜†๐˜๐—ต๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ: ๐—ง๐—ต๐—ฒ ๐—•๐—ฒ๐˜€๐˜ ๐—ฆ๐˜๐—ฎ๐—ฟ๐˜๐—ถ๐—ป๐—ด ๐—ฃ๐—ผ๐—ถ๐—ป๐˜ ๐—ณ๐—ผ๐—ฟ ๐—ง๐—ฒ๐—ฐ๐—ต & ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ ๐—•๐—ฒ๐—ด๐—ถ๐—ป๐—ป๐—ฒ๐—ฟ๐˜€๐Ÿ˜

๐Ÿš€ Want to break into tech or data analytics but donโ€™t know how to start?๐Ÿ“Œโœจ๏ธ

Python is the #1 most in-demand programming language, and Scalerโ€™s free Python for Beginners course is a game-changer for absolute beginners๐Ÿ“Šโœ”๏ธ

๐‹๐ข๐ง๐ค๐Ÿ‘‡:-

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No coding background needed!โœ…๏ธ
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๐Ÿง  Technologies for Data Analysts!

๐Ÿ“Š Data Manipulation & Analysis

โ–ช๏ธ Excel โ€“ Spreadsheet Data Analysis & Visualization
โ–ช๏ธ SQL โ€“ Structured Query Language for Data Extraction
โ–ช๏ธ Pandas (Python) โ€“ Data Analysis with DataFrames
โ–ช๏ธ NumPy (Python) โ€“ Numerical Computing for Large Datasets
โ–ช๏ธ Google Sheets โ€“ Online Collaboration for Data Analysis

๐Ÿ“ˆ Data Visualization

โ–ช๏ธ Power BI โ€“ Business Intelligence & Dashboarding
โ–ช๏ธ Tableau โ€“ Interactive Data Visualization
โ–ช๏ธ Matplotlib (Python) โ€“ Plotting Graphs & Charts
โ–ช๏ธ Seaborn (Python) โ€“ Statistical Data Visualization
โ–ช๏ธ Google Data Studio โ€“ Free, Web-Based Visualization Tool

๐Ÿ”„ ETL (Extract, Transform, Load)

โ–ช๏ธ SQL Server Integration Services (SSIS) โ€“ Data Integration & ETL
โ–ช๏ธ Apache NiFi โ€“ Automating Data Flows
โ–ช๏ธ Talend โ€“ Data Integration for Cloud & On-premises

๐Ÿงน Data Cleaning & Preparation

โ–ช๏ธ OpenRefine โ€“ Clean & Transform Messy Data
โ–ช๏ธ Pandas Profiling (Python) โ€“ Data Profiling & Preprocessing
โ–ช๏ธ DataWrangler โ€“ Data Transformation Tool

๐Ÿ“ฆ Data Storage & Databases

โ–ช๏ธ SQL โ€“ Relational Databases (MySQL, PostgreSQL, MS SQL)
โ–ช๏ธ NoSQL (MongoDB) โ€“ Flexible, Schema-less Data Storage
โ–ช๏ธ Google BigQuery โ€“ Scalable Cloud Data Warehousing
โ–ช๏ธ Redshift โ€“ Amazonโ€™s Cloud Data Warehouse

โš™๏ธ Data Automation

โ–ช๏ธ Alteryx โ€“ Data Blending & Advanced Analytics
โ–ช๏ธ Knime โ€“ Data Analytics & Reporting Automation
โ–ช๏ธ Zapier โ€“ Connect & Automate Data Workflows

๐Ÿ“Š Advanced Analytics & Statistical Tools

โ–ช๏ธ R โ€“ Statistical Computing & Analysis
โ–ช๏ธ Python (SciPy, Statsmodels) โ€“ Statistical Modeling & Hypothesis Testing
โ–ช๏ธ SPSS โ€“ Statistical Software for Data Analysis
โ–ช๏ธ SAS โ€“ Advanced Analytics & Predictive Modeling

๐ŸŒ Collaboration & Reporting

โ–ช๏ธ Power BI Service โ€“ Online Sharing & Collaboration for Dashboards
โ–ช๏ธ Tableau Online โ€“ Cloud-Based Visualization & Sharing
โ–ช๏ธ Google Analytics โ€“ Web Traffic Data Insights
โ–ช๏ธ Trello / JIRA โ€“ Project & Task Management for Data Projects
Data-Driven Decisions with the Right Tools!

React โค๏ธ for more
โค1
๐Ÿญ๐Ÿฌ๐Ÿฌ% ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—ง๐—ฒ๐—ฐ๐—ต ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€๐Ÿ˜

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โค1
Forwarded from Artificial Intelligence
๐Ÿฑ ๐—™๐—ฟ๐—ฒ๐—ฒ ๐—ฅ๐—ฒ๐˜€๐—ผ๐˜‚๐—ฟ๐—ฐ๐—ฒ๐˜€ ๐˜๐—ผ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป ๐— ๐—ฎ๐—ฐ๐—ต๐—ถ๐—ป๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐—ณ๐—ฟ๐—ผ๐—บ ๐—ฆ๐—ฐ๐—ฟ๐—ฎ๐˜๐—ฐ๐—ต ๐—ถ๐—ป ๐Ÿฎ๐Ÿฌ๐Ÿฎ๐Ÿฑ๐Ÿ˜

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10 ChatGPT Prompts for Marketing You'll Actually Use:

1. Crafting a Brand Story

Prompt: "Develop an engaging brand story and persona for [company name] that authentically conveys our mission, values, and competitive differentiation."

2. Optimizing Landing Pages

Prompt: "Optimize the landing page for [product/service page name] to immediately convey value, build trust, and motivate visitors to convert."

3. Generating Content Ideas

Prompt: "Suggest 10 compelling content ideas related to [topic/keyword] that will engage our target audience and promote brand awareness."

4. Improving SEO

Prompt: "Provide 3 strategic recommendations to improve SEO and increase organic traffic for [company name/website]."

5. Building an Email Nurture Sequence

Prompt: "Design a personalized, multi-message email nurture sequence focused on educating and converting leads interested in [product/service]."

6. Crafting Value Propositions

Prompt: "Create strong, benefit-driven value propositions highlighting the top 3 advantages of using [product/service] over competitors."

7. Producing Engaging Social Media Content

Prompt: "Develop 5 creative social media post ideas for [platform] that will resonate with our audience and align with broader campaign goals."

8. Planning an Influencer Marketing Partnership

Prompt: "Outline an influencer partnership strategy for [company] to build brand awareness and trust among our target demographic."

9. Crafting Paid Ads

Prompt: "Write high-converting Facebook/Google ads for [product/service] tailored to our unique selling proposition and audience motivations."

10. Building an Email Marketing Campaign

Prompt: "Construct a complete email marketing campaign for the launch of [product/service], including series copy, visual assets, send schedule, and success metrics."

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ENJOY LEARNING
๐Ÿ‘๐Ÿ‘
โค1
Forwarded from Artificial Intelligence
๐—™๐—ฟ๐—ฒ๐—ฒ ๐——๐—ฎ๐˜๐—ฎ ๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ ๐—ฅ๐—ผ๐—ฎ๐—ฑ๐—บ๐—ฎ๐—ฝ ๐—ณ๐—ผ๐—ฟ ๐—•๐—ฒ๐—ด๐—ถ๐—ป๐—ป๐—ฒ๐—ฟ๐˜€: ๐Ÿฑ ๐—ฆ๐˜๐—ฒ๐—ฝ๐˜€ ๐˜๐—ผ ๐—ฆ๐˜๐—ฎ๐—ฟ๐˜ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—๐—ผ๐˜‚๐—ฟ๐—ป๐—ฒ๐˜†๐Ÿ˜

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โค2