@Codeprogrammer Cheat Sheet Numpy.pdf
213.7 KB
This checklist covers the essentials of NumPy in one place, helping you:
- Create and initialize arrays
- Perform element-wise computations
- Stack and split arrays
- Apply linear algebra functions
- Efficiently index, slice, and manipulate arrays
…and much more!
Feel free to share if you found this useful, and let me know in the comments if I missed anything!
⚡️ BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
- Create and initialize arrays
- Perform element-wise computations
- Stack and split arrays
- Apply linear algebra functions
- Efficiently index, slice, and manipulate arrays
…and much more!
Feel free to share if you found this useful, and let me know in the comments if I missed anything!
#NumPy #Python #DataScience #MachineLearning #Automation #DeepLearning #Programming #Tech #DataAnalysis #SoftwareDevelopment #Coding #TechTips #PythonForDataScience
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Master Machine Learning in Just 20 Days.1745724742524
30.8 MB
Title:
Master Machine Learning in Just 20 Days - Your Ultimate Guide! 🔥
Description:
Struggling to break into Data Science or ace ML interviews at top product-based companies?
This 20-day roadmap covers ML basics to advanced topics like tuning, deep learning, and deployment with top resources and practice questions!
What’s Inside:
✅ Supervised & Unsupervised Learning – Regression, Classification, Clustering
✅ Deep Learning & Neural Networks – CNNs, RNNs, LSTMs
✅ End-to-End ML Projects – Data Preprocessing, Feature Engineering, Deployment
✅ Model Optimization – Hyperparameter Tuning, Ensemble Methods
✅ Real-World ML Applications – NLP, AutoML, Scalable ML Systems
#MachineLearning #DeepLearning #DataScience #ArtificialIntelligence #MLEngineering #CareerGrowth #MLRoadmap
By: t.me/HusseinSheikho✅
💯 BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
Master Machine Learning in Just 20 Days - Your Ultimate Guide! 🔥
Description:
Struggling to break into Data Science or ace ML interviews at top product-based companies?
This 20-day roadmap covers ML basics to advanced topics like tuning, deep learning, and deployment with top resources and practice questions!
What’s Inside:
✅ Supervised & Unsupervised Learning – Regression, Classification, Clustering
✅ Deep Learning & Neural Networks – CNNs, RNNs, LSTMs
✅ End-to-End ML Projects – Data Preprocessing, Feature Engineering, Deployment
✅ Model Optimization – Hyperparameter Tuning, Ensemble Methods
✅ Real-World ML Applications – NLP, AutoML, Scalable ML Systems
#MachineLearning #DeepLearning #DataScience #ArtificialIntelligence #MLEngineering #CareerGrowth #MLRoadmap
By: t.me/HusseinSheikho
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SciPy.pdf
206.4 KB
Unlock the full power of SciPy with my comprehensive cheat sheet!
Master essential functions for:
Function optimization and solving equations
Linear algebra operations
ODE integration and statistical analysis
Signal processing and spatial data manipulation
Data clustering and distance computation ...and much more!
💯 BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
Master essential functions for:
Function optimization and solving equations
Linear algebra operations
ODE integration and statistical analysis
Signal processing and spatial data manipulation
Data clustering and distance computation ...and much more!
#Python #SciPy #MachineLearning #DataScience #CheatSheet #ArtificialIntelligence #Optimization #LinearAlgebra #SignalProcessing #BigData
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Mastering CNNs: From Kernels to Model Evaluation
If you're learning Computer Vision, understanding the Conv2D layer in Convolutional Neural Networks (#CNNs) is crucial. Let’s break it down from basic to advanced.
1. What is Conv2D?
Conv2D is a 2D convolutional layer used in image processing. It takes an image as input and applies filters (also called kernels) to extract features.
2. What is a Kernel (or Filter)?
A kernel is a small matrix (like 3x3 or 5x5) that slides over the image and performs element-wise multiplication and summing.
A 3x3 kernel means the filter looks at 3x3 chunks of the image.
The kernel detects patterns like edges, textures, etc.
Example:
A vertical edge detection kernel might look like:
[-1, 0, 1]
[-1, 0, 1]
[-1, 0, 1]
3. What Are Filters in Conv2D?
In CNNs, we don’t use just one filter—we use multiple filters in a single Conv2D layer.
Each filter learns to detect a different feature (e.g., horizontal lines, curves, textures).
So if you have 32 filters in the Conv2D layer, you’ll get 32 feature maps.
More Filters = More Features = More Learning Power
4. Kernel Size and Its Impact
Smaller kernels (e.g., 3x3) are most common; they capture fine details.
Larger kernels (e.g., 5x5 or 7x7) capture broader patterns, but increase computational cost.
Many CNNs stack multiple small kernels (like 3x3) to simulate a large receptive field while keeping complexity low.
5. Life Cycle of a CNN Model (From Data to Evaluation)
Let’s visualize how a CNN model works from start to finish:
Step 1: Data Collection
Images are gathered and labeled (e.g., cat vs dog).
Step 2: Preprocessing
Resize images
Normalize pixel values
Data augmentation (flipping, rotation, etc.)
Step 3: Model Building (Conv2D layers)
Add Conv2D + Activation (ReLU)
Use Pooling layers (MaxPooling2D)
Add Dropout to prevent overfitting
Flatten and connect to Dense layers
Step 4: Training the Model
Feed data in batches
Use loss function (like cross-entropy)
Optimize using backpropagation + optimizer (like Adam)
Adjust weights over several epochs
Step 5: Evaluation
Test the model on unseen data
Use metrics like Accuracy, Precision, Recall, F1-Score
Visualize using confusion matrix
Step 6: Deployment
Convert model to suitable format (e.g., ONNX, TensorFlow Lite)
Deploy on web, mobile, or edge devices
Summary
Conv2D uses filters (kernels) to extract image features.
More filters = better feature detection.
The CNN pipeline takes raw image data, learns features, and gives powerful predictions.
If this helped you, let me know! Or feel free to share your experience learning CNNs!
💯 BEST DATA SCIENCE CHANNELS ON TELEGRAM 🌟
If you're learning Computer Vision, understanding the Conv2D layer in Convolutional Neural Networks (#CNNs) is crucial. Let’s break it down from basic to advanced.
1. What is Conv2D?
Conv2D is a 2D convolutional layer used in image processing. It takes an image as input and applies filters (also called kernels) to extract features.
2. What is a Kernel (or Filter)?
A kernel is a small matrix (like 3x3 or 5x5) that slides over the image and performs element-wise multiplication and summing.
A 3x3 kernel means the filter looks at 3x3 chunks of the image.
The kernel detects patterns like edges, textures, etc.
Example:
A vertical edge detection kernel might look like:
[-1, 0, 1]
[-1, 0, 1]
[-1, 0, 1]
3. What Are Filters in Conv2D?
In CNNs, we don’t use just one filter—we use multiple filters in a single Conv2D layer.
Each filter learns to detect a different feature (e.g., horizontal lines, curves, textures).
So if you have 32 filters in the Conv2D layer, you’ll get 32 feature maps.
More Filters = More Features = More Learning Power
4. Kernel Size and Its Impact
Smaller kernels (e.g., 3x3) are most common; they capture fine details.
Larger kernels (e.g., 5x5 or 7x7) capture broader patterns, but increase computational cost.
Many CNNs stack multiple small kernels (like 3x3) to simulate a large receptive field while keeping complexity low.
5. Life Cycle of a CNN Model (From Data to Evaluation)
Let’s visualize how a CNN model works from start to finish:
Step 1: Data Collection
Images are gathered and labeled (e.g., cat vs dog).
Step 2: Preprocessing
Resize images
Normalize pixel values
Data augmentation (flipping, rotation, etc.)
Step 3: Model Building (Conv2D layers)
Add Conv2D + Activation (ReLU)
Use Pooling layers (MaxPooling2D)
Add Dropout to prevent overfitting
Flatten and connect to Dense layers
Step 4: Training the Model
Feed data in batches
Use loss function (like cross-entropy)
Optimize using backpropagation + optimizer (like Adam)
Adjust weights over several epochs
Step 5: Evaluation
Test the model on unseen data
Use metrics like Accuracy, Precision, Recall, F1-Score
Visualize using confusion matrix
Step 6: Deployment
Convert model to suitable format (e.g., ONNX, TensorFlow Lite)
Deploy on web, mobile, or edge devices
Summary
Conv2D uses filters (kernels) to extract image features.
More filters = better feature detection.
The CNN pipeline takes raw image data, learns features, and gives powerful predictions.
If this helped you, let me know! Or feel free to share your experience learning CNNs!
#DeepLearning #ComputerVision #CNNs #Conv2D #MachineLearning #AI #NeuralNetworks #DataScience #ModelTraining #ImageProcessing
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Dive deep into the world of Transformers with this comprehensive PyTorch implementation guide. Whether you're a seasoned ML engineer or just starting out, this resource breaks down the complexities of the Transformer model, inspired by the groundbreaking paper "Attention Is All You Need".
https://www.k-a.in/pyt-transformer.html
This guide offers:
By following along, you'll gain a solid understanding of how Transformers work and how to implement them from scratch.
#MachineLearning #DeepLearning #PyTorch #Transformer #AI #NLP #AttentionIsAllYouNeed #Coding #DataScience #NeuralNetworks
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Top 100+ questions%0A %22Google Data Science Interview%22.pdf
16.7 MB
Google is known for its rigorous data science interview process, which typically follows a hybrid format. Candidates are expected to demonstrate strong programming skills, solid knowledge in statistics and machine learning, and a keen ability to approach problems from a product-oriented perspective.
To succeed, one must be proficient in several critical areas: statistics and probability, SQL and Python programming, product sense, and case study-based analytics.
This curated list features over 100 of the most commonly asked and important questions in Google data science interviews. It serves as a comprehensive resource to help candidates prepare effectively and confidently for the challenge ahead.
#DataScience #GoogleInterview #InterviewPrep #MachineLearning #SQL #Statistics #ProductAnalytics #Python #CareerGrowth
https://t.me/addlist/0f6vfFbEMdAwODBk
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@CodeProgrammer Matplotlib.pdf
4.3 MB
The Complete Visual Guide for Data Enthusiasts
Matplotlib is a powerful Python library for data visualization, essential not only for acing job interviews but also for building a solid foundation in analytical thinking and data storytelling.
This step-by-step tutorial guide walks learners through everything from the basics to advanced techniques in Matplotlib. It also includes a curated collection of the most frequently asked Matplotlib-related interview questions, making it an ideal resource for both beginners and experienced professionals.
#Matplotlib #DataVisualization #Python #DataScience #InterviewPrep #Analytics #TechCareer #LearnToCode
https://t.me/addlist/0f6vfFbEMdAwODBk
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Introduction to Machine Learning” by Alex Smola and S.V.N.
Vishwanathan is a foundational textbook that offers a comprehensive and mathematically rigorous introduction to core concepts in machine learning. The book covers key topics including supervised and unsupervised learning, kernels, graphical models, optimization techniques, and large-scale learning. It balances theory and practical application, making it ideal for graduate students, researchers, and professionals aiming to deepen their understanding of machine learning fundamentals and algorithmic principles.
PDF:
https://alex.smola.org/drafts/thebook.pdf
Vishwanathan is a foundational textbook that offers a comprehensive and mathematically rigorous introduction to core concepts in machine learning. The book covers key topics including supervised and unsupervised learning, kernels, graphical models, optimization techniques, and large-scale learning. It balances theory and practical application, making it ideal for graduate students, researchers, and professionals aiming to deepen their understanding of machine learning fundamentals and algorithmic principles.
PDF:
https://alex.smola.org/drafts/thebook.pdf
#MachineLearning #AI #DataScience #MLAlgorithms #DeepLearning #MathForML #MLTheory #MLResearch #AlexSmola #SVNVishwanathan
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Machine Learning Notes 📝 (1).pdf
4.9 MB
Machine Learning Notes with Real Project and Amazing discussion
https://t.me/CodeProgrammer🌟
#MachineLearning #AI #DataScience #MLAlgorithms #DeepLearning
https://t.me/CodeProgrammer
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9 machine learning concepts for ML engineers!
(explained as visually as possible)
Here's a recap of several visual summaries posted in the Daily Dose of Data Science newsletter.
1️⃣ 4 strategies for Multi-GPU Training.
- Training at scale? Learn these strategies to maximize efficiency and minimize model training time.
- Read here: https://lnkd.in/gmXF_PgZ
2️⃣ 4 ways to test models in production
- While testing a model in production might sound risky, ML teams do it all the time, and it isn’t that complicated.
- Implemented here: https://lnkd.in/g33mASMM
3️⃣ Training & inference time complexity of 10 ML algorithms
Understanding the run time of ML algorithms is important because it helps you:
- Build a core understanding of an algorithm.
- Understand the data-specific conditions to use the algorithm
- Read here: https://lnkd.in/gKJwJ__m
4️⃣ Regression & Classification Loss Functions.
- Get a quick overview of the most important loss functions and when to use them.
- Read here: https://lnkd.in/gzFPBh-H
5️⃣ Transfer Learning, Fine-tuning, Multitask Learning, and Federated Learning.
- The holy grail of advanced learning paradigms, explained visually.
- Learn about them here: https://lnkd.in/g2hm8TMT
6️⃣ 15 Pandas to Polars to SQL to PySpark Translations.
- The visual will help you build familiarity with four popular frameworks for data analysis and processing.
- Read here: https://lnkd.in/gP-cqjND
7️⃣ 11 most important plots in data science
- A must-have visual guide to interpret and communicate your data effectively.
- Explained here: https://lnkd.in/geMt98tF
8️⃣ 11 types of variables in a dataset
Understand and categorize dataset variables for better feature engineering.
- Explained here: https://lnkd.in/gQxMhb_p
9️⃣ NumPy cheat sheet for data scientists
- The ultimate cheat sheet for fast, efficient numerical computing in Python.
- Read here: https://lnkd.in/gbF7cJJE
🔗 Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk
📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
(explained as visually as possible)
Here's a recap of several visual summaries posted in the Daily Dose of Data Science newsletter.
- Training at scale? Learn these strategies to maximize efficiency and minimize model training time.
- Read here: https://lnkd.in/gmXF_PgZ
- While testing a model in production might sound risky, ML teams do it all the time, and it isn’t that complicated.
- Implemented here: https://lnkd.in/g33mASMM
Understanding the run time of ML algorithms is important because it helps you:
- Build a core understanding of an algorithm.
- Understand the data-specific conditions to use the algorithm
- Read here: https://lnkd.in/gKJwJ__m
- Get a quick overview of the most important loss functions and when to use them.
- Read here: https://lnkd.in/gzFPBh-H
- The holy grail of advanced learning paradigms, explained visually.
- Learn about them here: https://lnkd.in/g2hm8TMT
- The visual will help you build familiarity with four popular frameworks for data analysis and processing.
- Read here: https://lnkd.in/gP-cqjND
- A must-have visual guide to interpret and communicate your data effectively.
- Explained here: https://lnkd.in/geMt98tF
Understand and categorize dataset variables for better feature engineering.
- Explained here: https://lnkd.in/gQxMhb_p
- The ultimate cheat sheet for fast, efficient numerical computing in Python.
- Read here: https://lnkd.in/gbF7cJJE
#MachineLearning #DataScience #MLEngineering #DeepLearning #AI #MLOps #BigData #Python #NumPy #Pandas #Visualization
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from SQL to pandas.pdf
1.3 MB
#DataScience #SQL #pandas #InterviewPrep #Python #DataAnalysis #CareerGrowth #TechTips #Analytics
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Numpy from basics to advanced.pdf
2.4 MB
NumPy is an essential library in the world of data science, widely recognized for its efficiency in numerical computations and data manipulation. This powerful tool simplifies complex operations with arrays, offering a faster and cleaner alternative to traditional Python lists and loops.
The "Mastering NumPy" booklet provides a comprehensive walkthrough—from array creation and indexing to mathematical/statistical operations and advanced topics like reshaping and stacking. All concepts are illustrated with clear, beginner-friendly examples, making it ideal for anyone aiming to boost their data handling skills.
#NumPy #Python #DataScience #MachineLearning #AI #BigData #DeepLearning #DataAnalysis
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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🚀 DataCamp has officially partnered with Polars**—a cutting-edge DataFrame library designed for speed and efficiency!
To mark this exciting collaboration, **DataCamp is offering free access to its brand-new course *“Introduction to Polars”* for the next 90 days. 🎉
This course is a great opportunity for learners and professionals alike to master data cleaning, transformation, and analysis with Polars' high-performance engine, lazy execution, and powerful groupby operations.
Unlock the full potential of data workflows and explore how Polars can supercharge large-scale data processing.
🔗 Start learning now:
https://www.datacamp.com/courses/introduction-to-polars
🌟 Join the communities:
To mark this exciting collaboration, **DataCamp is offering free access to its brand-new course *“Introduction to Polars”* for the next 90 days. 🎉
This course is a great opportunity for learners and professionals alike to master data cleaning, transformation, and analysis with Polars' high-performance engine, lazy execution, and powerful groupby operations.
Unlock the full potential of data workflows and explore how Polars can supercharge large-scale data processing.
🔗 Start learning now:
https://www.datacamp.com/courses/introduction-to-polars
#DataScience #Polars #Python #BigData #DataEngineering #MachineLearning #DataAnalytics #OpenSource #DataCamp #FreeCourse #LearnDataScience
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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python_basics.pdf
212.3 KB
I've just compiled a set of clean and powerful Python Cheat Sheets to help beginners and intermediates speed up their coding workflow.
Whether you're brushing up on the basics or diving into data science, these sheets will save you time and boost your productivity.
Python Basics
Jupyter Notebook Tips
Importing Libraries
NumPy Essentials
Pandas Overview
Perfect for students, developers, and anyone looking to keep essential Python knowledge at their fingertips.
#Python #CheatSheets #PythonTips #DataScience #JupyterNotebook #NumPy #Pandas #MachineLearning #AI #CodingTips #PythonForBeginners
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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#DataScience #HowToBecomeADataScientist #ML2025 #Python #SQL #MachineLearning #MathForDataScience #BigData #MLOps #DeepLearning #AIResearch #DataVisualization #PortfolioProjects #CloudComputing #DSCareerPath
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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👨🏻💻 I've been collecting a variety of data science interview questions for different positions for a few weeks now.
Common Data Science and ML Questions (34 questions)
Regression (22 questions)
Classification (39 questions)
SVM algorithms, decision tree
Simple Bayes and statistical discussions and...
┌
└
#DataScience #InterviewPrep #MLInterviews #DataScientist #MachineLearning #TechCareers #DSInterviewQuestions #GitHubResources #CareerInDataScience #CodingInterview
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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Supervised Learning: Classification and Regression
Download: https://faculty.ucmerced.edu/mcarreira-perpinan/teaching/CSE176/lecturenotes.pdf
Download: https://faculty.ucmerced.edu/mcarreira-perpinan/teaching/CSE176/lecturenotes.pdf
#SupervisedLearning #MachineLearning #Classification #Regression #MLNotes #DataScience #AIResources #MLTheory #MLLectures #LearnML
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👨🏻💻 Real learning means implementing ideas and building prototypes. It's time to skip the repetitive training and get straight to real data science projects!
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└
#DataScience #PythonProjects #MachineLearning #DeepLearning #AIProjects #RealWorldData #OpenSource #DataAnalysis #ProjectBasedLearning #LearnByBuilding
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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𝗬𝗼𝘂𝗿_𝗗𝗮𝘁𝗮_𝗦𝗰𝗶𝗲𝗻𝗰𝗲_𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄_𝗦𝘁𝘂𝗱𝘆_𝗣𝗹𝗮𝗻.pdf
7.7 MB
1. Master the fundamentals of Statistics
Understand probability, distributions, and hypothesis testing
Differentiate between descriptive vs inferential statistics
Learn various sampling techniques
2. Get hands-on with Python & SQL
Work with data structures, pandas, numpy, and matplotlib
Practice writing optimized SQL queries
Master joins, filters, groupings, and window functions
3. Build real-world projects
Construct end-to-end data pipelines
Develop predictive models with machine learning
Create business-focused dashboards
4. Practice case study interviews
Learn to break down ambiguous business problems
Ask clarifying questions to gather requirements
Think aloud and structure your answers logically
5. Mock interviews with feedback
Use platforms like Pramp or connect with peers
Record and review your answers for improvement
Gather feedback on your explanation and presence
6. Revise machine learning concepts
Understand supervised vs unsupervised learning
Grasp overfitting, underfitting, and bias-variance tradeoff
Know how to evaluate models (precision, recall, F1-score, AUC, etc.)
7. Brush up on system design (if applicable)
Learn how to design scalable data pipelines
Compare real-time vs batch processing
Familiarize with tools: Apache Spark, Kafka, Airflow
8. Strengthen storytelling with data
Apply the STAR method in behavioral questions
Simplify complex technical topics
Emphasize business impact and insight-driven decisions
9. Customize your resume and portfolio
Tailor your resume for each job role
Include links to projects or GitHub profiles
Match your skills to job descriptions
10. Stay consistent and track progress
Set clear weekly goals
Monitor covered topics and completed tasks
Reflect regularly and adapt your plan as needed
#DataScience #InterviewPrep #MLInterviews #DataEngineering #SQL #Python #Statistics #MachineLearning #DataStorytelling #SystemDesign #CareerGrowth #DataScienceRoadmap #PortfolioBuilding #MockInterviews #JobHuntingTips
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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