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Closures & Decorators in Python 👆
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🚀 Learn API development with Python

🔑 Core Skills Before API Development

Python Fundamentals
- Functions, classes, error handling
- JSON handling (json module)
- Virtual environments (venv, pipenv, or poetry)

HTTP Basics
- What is an API? (REST, GraphQL, gRPC basics)
- HTTP methods: GET, POST, PUT, PATCH, DELETE
- Status codes: 200, 201, 400, 401, 404, 500


🛠️ API Development with Python

1. Frameworks
- Flask (lightweight, easy for beginners)
- FastAPI (modern, async, automatic docs with Swagger/OpenAPI – highly recommended)
- Django REST Framework (DRF) (for large apps with Django)
👉 Start with FastAPI if your goal is modern, production-ready APIs.

2. Core Concepts
- Routing (endpoints like /users, /products)
- Path & query parameters
- Request & response handling (JSON input/output)
- Middleware (logging, authentication, error handling)


3. Data & Persistence
- Working with databases:
- SQL (PostgreSQL, MySQL, SQLite)
- ORMs: SQLAlchemy or Django ORM
- CRUD operations with database integration


4. Authentication & Security
- JWT (JSON Web Tokens)
- OAuth2 (Google, GitHub login)
- API key-based authentication
- CORS handling


5. Testing & Documentation
- Writing tests with pytest or unittest
- Automatic API docs (FastAPI auto-generates Swagger UI)
- Postman or cURL for testing endpoints


6. Deployment & Scaling
- Running APIs with Uvicorn or Gunicorn
- Containerization with Docker
- CI/CD (GitHub Actions, GitLab CI)
- Cloud deployment (AWS, GCP, Azure, or Heroku)


📚 Suggested Learning Path:
Learn FastAPI → build a simple "To-Do API"
Connect a database → PostgreSQL + SQLAlchemy
Add authentication → JWT-based login
Write tests → pytest for endpoints
Deploy on Docker + Cloud
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10 Most Useful SQL Interview Queries (with Examples) 💼

1️⃣ Find the second highest salary:
SELECT MAX(salary)  
FROM employees 
WHERE salary < (SELECT MAX(salary) FROM employees);


2️⃣ Count employees in each department:
SELECT department, COUNT(*)  
FROM employees 
GROUP BY department;


3️⃣ Fetch duplicate emails:
SELECT email, COUNT(*)  
FROM users 
GROUP BY email 
HAVING COUNT(*) > 1;


4️⃣ Join orders with customer names:
SELECT c.name, o.order_date  
FROM customers c 
JOIN orders o ON c.id = o.customer_id;


5️⃣ Get top 3 highest salaries:
SELECT DISTINCT salary  
FROM employees 
ORDER BY salary DESC 
LIMIT 3;


6️⃣ Retrieve latest 5 logins:
SELECT * FROM logins  
ORDER BY login_time DESC 
LIMIT 5;


7️⃣ Employees with no manager:
SELECT name  
FROM employees 
WHERE manager_id IS NULL;


8️⃣ Search names starting with ‘S’:
SELECT * FROM employees  
WHERE name LIKE 'S%';


9️⃣ Total sales per month:
SELECT MONTH(order_date) AS month, SUM(amount)  
FROM sales 
GROUP BY MONTH(order_date);


🔟 Delete inactive users:
DELETE FROM users  
WHERE last_active < '2023-01-01';


Tip: Master subqueries, joins, groupings & filters – they show up in nearly every interview!

💬 Tap ❤️ for more!
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Best Code Editors For Python 👨‍💻
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🔥 Guys, Another Big Announcement!

I’m launching a Python Interview Series 🐍💼 — your complete guide to cracking Python interviews from beginner to advanced level!

This will be a week-by-week series designed to make you interview-ready — covering core concepts, coding questions, and real interview scenarios asked by top companies.

Here’s what’s coming your way 👇

🔹 Week 1: Python Fundamentals (Beginner Level)
• Data types, variables & operators
• If-else, loops & functions
• Input/output & basic problem-solving
💡 *Practice:* Reverse string, Prime check, Factorial, Palindrome

🔹 Week 2: Data Structures in Python
• Lists, Tuples, Sets, Dictionaries
• Comprehensions (list, dict, set)
• Sorting, searching, and nested structures
💡 *Practice:* Frequency count, remove duplicates, find max/min

🔹 Week 3: Functions, Modules & File Handling
*args, *kwargs, lambda, map/filter/reduce
• File read/write, CSV handling
• Modules & imports
💡 *Practice:* Create custom functions, read data files, handle errors

🔹 Week 4: Object-Oriented Programming (OOP)
• Classes, objects, inheritance, polymorphism
• Encapsulation & abstraction
• Magic methods (__init__, __str__)
💡 *Practice:* Build a simple class like BankAccount or StudentSystem

🔹 Week 5: Exception Handling & Logging
try-except-else-finally
• Custom exceptions
• Logging errors & debugging best practices
💡 *Practice:* File operations with proper error handling

🔹 Week 6: Advanced Python Concepts
• Decorators, generators, iterators
• Closures & context managers
• Shallow vs deep copy
💡 *Practice:* Create your own decorator, generator examples

🔹 Week 7: Pandas & NumPy for Data Analysis
• DataFrame basics, filtering & grouping
• Handling missing data
• NumPy arrays, slicing, and aggregation
💡 *Practice:* Analyze small CSV datasets

🔹 Week 8: Python for Analytics & Visualization
• Matplotlib, Seaborn basics
• Data summarization & correlation
• Building simple dashboards
💡 *Practice:* Visualize sales or user data

🔹 Week 9: Real Interview Questions (Intermediate–Advanced)
• 50+ Python interview questions with answers
• Common logical & coding tasks
• Real company-style questions (Infosys, TCS, Deloitte, etc.)
💡 *Practice:* Solve daily problem sets

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• End-to-end mock interviews
• Python project discussion tips
• Resume & GitHub portfolio guidance

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Coding Snippets & Practice Tasks
Real Interview Q&A
Mini Quiz & Discussion

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Artificial Intelligence Engineer Roadmap 🤖🧠

🚀 Foundations
- Mathematics
• Linear Algebra, Calculus
• Probability & Statistics
- Programming
• Python (core language)
• C++ (for performance)
• SQL (for data handling)
- Computer Science Basics
• Data Structures & Algorithms
• OOP Concepts

📘 Core AI Concepts
- Search Algorithms
• BFS, DFS, A*
- Knowledge Representation
• Ontologies, Graphs
- Logic & Reasoning
• Propositional & Predicate Logic
- Planning & Decision Making
• Markov Decision Process (MDP)
• Game Theory Basics

🧠 Machine Learning & Deep Learning
- ML Algorithms
• Regression, Classification, Clustering
- Deep Learning
• Neural Networks, CNN, RNN
• Transformers, Attention Mechanisms
- Frameworks
• TensorFlow, PyTorch, Keras

📊 NLP & Computer Vision
- NLP
• Tokenization, Lemmatization
• Language Models (BERT, )
- CV
• Image Classification, Object Detection
• OpenCV, YOLO, Mask R-CNN

🛠 Tools & Platforms
- Jupyter, GitHub, Docker
- MLflow, Weights & Biases
- Hugging Face, OpenAI APIs

☁️ Model Deployment & Monitoring
- FastAPI, Flask for APIs
- CI/CD Pipelines
- Cloud (AWS Sagemaker, GCP Vertex AI, Azure ML)

🧑‍💼 Real-World Essentials
- AI Product Thinking
- Explainable AI (XAI)
- Ethics, Bias & Fairness
- Working with Stakeholders

📚 Learn From
- Papers with Code
- Arxiv.org
- DeepLearning.AI
- Kaggle Projects
- YouTube Lectures (e.g. MIT, Stanford)

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📈 Predictive Modeling for Future Stock Prices in Python: A Step-by-Step Guide

The process of building a stock price prediction model using Python.

1. Import required modules

2. Obtaining historical data on stock prices

3. Selection of features.

4. Definition of features and target variable

5. Preparing data for training

6. Separation of data into training and test sets

7. Building and training the model

8. Making forecasts

9. Trading Strategy Testing
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