ACupOfTea

# aCupOfTea
# AI Tea Lounge: Sipping Knowledge in AI Domains
![image](https://github.com/AhmedYousriSobhi/aCupOfTea/assets/66730765/4a033ba8-5aac-475d-9b27-ec13e68746ba)

Welcome to the AI Tea Lounge, a cozy space to settle down, share experiences, and indulge in the world of artificial intelligence. In this repository, we've brewed a blend of insights and expertise spanning across Data Science, Machine Learning, Deep Learning, and a wide spectrum of AI-related topics. From programming and clean code practices to design patterns and business implications, we invite you to join us for a cup of wisdom.

## Table of Contents
- [aCupOfTea](#acupoftea)
- [AI Tea Lounge: Sipping Knowledge in AI Domains](#ai-tea-lounge-sipping-knowledge-in-ai-domains)
  - [Table of Contents](#table-of-contents)
  - [About](#about)
  - [Fields](#fields)
  - [Topics](#topics)
  - [Structure](#structure)
  - [Contributing](#contributing)
  - [Sip and Support](#sip-and-support)

## About
The AI Tea Lounge is your hub for expanding your understanding of various AI domains. Whether you're an AI enthusiast, a curious learner, or a seasoned practitioner, you'll find a comfortable space to explore, share, and learn from the collective experiences of the community.

## Fields
Dive into the depths of AI through the lenses of different fields:

- Data Science: Unearth insights from data, transform raw information into actionable intelligence, and make informed decisions.

- Machine Learning: Unveil the magic of algorithms that learn from data and improve their performance over time.

- Deep Learning: Delve into the intricacies of neural networks, artificial neural pathways that mimic the human brain's way of processing information.

## Topics
From the aroma of well-structured code to the rich flavors of design patterns and real-world business implications, our repository covers a wide array of topics:

- Programming and Clean Code: Discover the art of writing elegant, efficient, and maintainable code that stands the test of time.

- Design Patterns: Explore tried-and-true design solutions to common problems, fostering scalability, flexibility, and maintainability.

- Business Interference: Grasp the intricate dance between AI and business, how AI technologies impact decision-making, and the potential for innovation.

## Structure
```bash
.
├── business
|   ├── foodDeliveryServiceCompany
|   ├── realStateCompany
|   ├── usedCarsRetailer
├── fields
|   ├── dataCollection
|   ├── deepLearning
|   |   ├── conceptsInDeepLearning
|   |   |   ├── basics
|   |   |   ├── convolution
|   |   |   ├── sequenceModels
|   |   |   ├── historyOfAI
|   |   |   ├── stateOfArt
|   |   ├── workspace
|   ├── designPatterns
|   |   ├── featureCross
|   ├── generativeAI
|   |   ├── evaluation_and_debugging
|   |   ├── langchain
|   |   ├── llm_VsCode
|   |   ├── Machine_translator
|   ├── libarariesAndFrameworks
|   |   ├── pandas
|   |   ├── pytorch
|   ├── projectTopics
|   |   ├── CustomerSegmentation
|   ├── recommenderSystems
|   ├── reinforcementLearning
|   |   ├── conceptsInReinforcementLearning
|   |   |   ├── basics
|   ├── statistics
|   ├── tabularData
|   |   ├── conceptInMachineLearning
|   |   |   ├── dataEDAanalysis
|   |   |   ├── mlSupervisedClassification
|   |   |   ├── mlSupervisedRegression
|   |   |   ├── mlUnsupervised
|   |   ├── conceptInTimeSeries
|   |   ├── dataProcessing
|   |   |   ├── handleDuplicatedData
|   |   |   ├── handlingMissingData
|   |   |   |   ├── determineMissingValues
|   |   |   |   ├── regressionImputing
|   |   |   |   ├── meanImputing
|   |   |   ├── handlingObjectdata
|   |   |   |   ├── wordsSimilarity
|   |   |   ├── handlingOutliers
|   |   |   ├── handlingSkewness
|   |   ├── dataEDA
|   |   |   ├── correlalstion
|   |   |   |   ├── detectmulticollinearity
|   |   |   |   ├── extremeCorrelation
|   |   |   ├── analysisPloting
|   |   |   ├── featureAnalysis
|   |   |   ├── visualizationGraphs
|   |   ├── dataEvaluation
|   |   |   ├── classification
|   |   |   |   ├── accuracyParadox
|   |   |   ├── clustering
|   |   |   ├── modelBehaviour
|   |   |   |   ├── biasVarienceTradeOff
|   |   |   ├── regression
|   |   ├── dataFeatureEngineering
|   |   |   |   ├── PCA
|   |   ├── dataModeling
|   |   |   ├── clustering
|   |   |   ├── pipeline
|   |   |   ├── xgboost
├── Problems
|   ├── problem_solving
|   ├── question_CheapestFlatsPerCityUsingSQL
|   ├── question_JobCounterUsingPySpark
|   ├── question_MachineLearningClassifier
├── programming
|   ├── dataStructure
|   ├── decorators
|   ├── languages
|   ├── oop
|   ├── python
|   ├── pythonCleanCode
|   ├── softwareGoals
|   |   ├── robustness
├── projects
|   ├── BEV-Project
|   ├── customer_segmentation
|   ├── face-off
|   ├── market_campain_imapct
|   ├── used_cars_price_estimation
├── tips
└── README.md
```

## Contributing
The AI A Cup of Tea thrives on collaboration and diverse perspectives. If you have insights to share, solutions to optimize, or new topics to add to the menu, we encourage you to contribute. Open a pull request and let's continue building this cozy haven of knowledge together.

## Sip and Support
If you find a thought-provoking idea or a solution that resonates with you, consider offering us a cup of tea by sharing your thoughts, insights, or even improvements. Let's nurture a space where ideas flow freely, and our collective sips of wisdom create a harmonious symphony of AI knowledge.

<div id="header">
  <img src="https://media.giphy.com/media/KZMRyVjEtdv8AU6mIr/giphy.gif" width="350"/>
</div>

__Cheers to knowledge, growth, and a cup of AI-infused tea! 🍵🤖__